Overview

Brought to you by YData

Dataset statistics

Number of variables	55
Number of observations	300
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	1.3 MiB
Average record size in memory	4.5 KiB

Variable types

Text	18
Numeric	1
Categorical	36

Alerts

`Authority_Present` is highly overall correlated with `Clarity_and_Conciseness_Value` and 35 other fields	High correlation
`Clarity_and_Conciseness_Value` is highly overall correlated with `Authority_Present` and 32 other fields	High correlation
`Contains_Colon` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Contains_Exclamation_Mark` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Contains_Hyphen` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Contains_Numbers` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Contains_Question_Mark` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Contains_Quotes` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Curiosity_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Economic_Benefit_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Economic_Benefit_Words` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`Emphatic_Capitalization_Usage` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`Ends_With_Question_Mark` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Exclusivity_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Exclusivity_Words` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`Fear_Concern_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Hope_Optimism_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Indignation_Controversy_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Length_General_Assessment` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Main_Category` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`Main_Classification` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`National_Relevance_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Originality_and_Differentiation_Value` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Personal_Identification_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Prohibition_Restriction_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Prohibition_Restriction_Words` is highly overall correlated with `Authority_Present` and 26 other fields	High correlation
`Recognized_Brand_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Relevance_and_Timeliness_Value` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Solution_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Solution_Words` is highly overall correlated with `Authority_Present` and 26 other fields	High correlation
`Starts_With_Number` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Strategic_Keyword_Usage_Value` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Subcategory_2` is highly overall correlated with `Authority_Present` and 26 other fields	High correlation
`Surprise_Awe_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Temporal_Urgency_Present` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Temporal_Urgency_Words` is highly overall correlated with `Authority_Present` and 27 other fields	High correlation
`Visibility` is highly overall correlated with `Authority_Present` and 35 other fields	High correlation
`Subcategory_2` is highly imbalanced (57.1%)	Imbalance
`Clarity_and_Conciseness_Value` is highly imbalanced (88.3%)	Imbalance
`Relevance_and_Timeliness_Value` is highly imbalanced (80.1%)	Imbalance
`Strategic_Keyword_Usage_Value` is highly imbalanced (83.4%)	Imbalance
`Contains_Quotes` is highly imbalanced (56.8%)	Imbalance
`Contains_Question_Mark` is highly imbalanced (77.1%)	Imbalance
`Contains_Colon` is highly imbalanced (56.8%)	Imbalance
`Contains_Exclamation_Mark` is highly imbalanced (87.8%)	Imbalance
`Starts_With_Number` is highly imbalanced (69.3%)	Imbalance
`Ends_With_Question_Mark` is highly imbalanced (79.0%)	Imbalance
`Length_General_Assessment` is highly imbalanced (71.7%)	Imbalance
`Emphatic_Capitalization_Usage` is highly imbalanced (85.3%)	Imbalance
`Temporal_Urgency_Present` is highly imbalanced (54.1%)	Imbalance
`Temporal_Urgency_Words` is highly imbalanced (71.6%)	Imbalance
`Exclusivity_Present` is highly imbalanced (82.2%)	Imbalance
`Exclusivity_Words` is highly imbalanced (88.7%)	Imbalance
`Solution_Present` is highly imbalanced (51.1%)	Imbalance
`Solution_Words` is highly imbalanced (69.5%)	Imbalance
`Economic_Benefit_Present` is highly imbalanced (55.7%)	Imbalance
`Economic_Benefit_Words` is highly imbalanced (73.2%)	Imbalance
`Prohibition_Restriction_Present` is highly imbalanced (62.9%)	Imbalance
`Prohibition_Restriction_Words` is highly imbalanced (78.3%)	Imbalance
`Indignation_Controversy_Present` is highly imbalanced (50.1%)	Imbalance
`Title` has unique values	Unique
`Visibility` has unique values	Unique

Reproduction

Analysis started	2025-07-02 14:31:14.805314
Analysis finished	2025-07-02 14:31:25.021576
Duration	10.22 seconds
Software version	ydata-profiling vv4.16.1
Download configuration	config.json

Title
Text

Unique

Distinct	300
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	52.7 KiB

Length

Max length	239
Median length	120.5
Mean length	83.82
Min length	20

Characters and Unicode

Total characters	25146
Distinct characters	86
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	300 ?
Unique (%)	100.0%

Sample

1st row	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd row	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd row	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th row	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th row	McDonald's Removing 1 Breakfast Menu Item for Good on July 2

Value	Count	Frequency (%)
to	126	3.0%
the	101	2.4%
in	79	1.9%
for	67	1.6%
and	61	1.5%
new	54	1.3%
of	54	1.3%
a	52	1.2%
with	34	0.8%
is	30	0.7%
Other values (1812)	3516	84.2%

Most occurring characters

Value	Count	Frequency (%)
	3874	15.4%
e	2311	9.2%
a	1532	6.1%
o	1485	5.9%
t	1449	5.8%
r	1395	5.5%
n	1377	5.5%
i	1343	5.3%
s	1231	4.9%
l	877	3.5%
Other values (76)	8272	32.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	25146	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	3874	15.4%
e	2311	9.2%
a	1532	6.1%
o	1485	5.9%
t	1449	5.8%
r	1395	5.5%
n	1377	5.5%
i	1343	5.3%
s	1231	4.9%
l	877	3.5%
Other values (76)	8272	32.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	25146	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	3874	15.4%
e	2311	9.2%
a	1532	6.1%
o	1485	5.9%
t	1449	5.8%
r	1395	5.5%
n	1377	5.5%
i	1343	5.3%
s	1231	4.9%
l	877	3.5%
Other values (76)	8272	32.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	25146	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	3874	15.4%
e	2311	9.2%
a	1532	6.1%
o	1485	5.9%
t	1449	5.8%
r	1395	5.5%
n	1377	5.5%
i	1343	5.3%
s	1231	4.9%
l	877	3.5%
Other values (76)	8272	32.9%

Visibility
Real number (ℝ)

High correlation Unique

Distinct	300
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4500275.1

Minimum	1993759
Maximum	22572066
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.5 KiB

Quantile statistics

Minimum	1993759
5-th percentile	2069848.6
Q1	2471328
median	3352569
Q3	5180321.2
95-th percentile	10804184
Maximum	22572066
Range	20578307
Interquartile range (IQR)	2708993.2

Descriptive statistics

Standard deviation	3231247.7
Coefficient of variation (CV)	0.71801113
Kurtosis	8.4699825
Mean	4500275.1
Median Absolute Deviation (MAD)	1042408.5
Skewness	2.5928748
Sum	1.3500825 × 10⁹
Variance	1.0440961 × 10¹³
Monotonicity	Strictly decreasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1993759	1	0.3%
22572066	1	0.3%
21331409	1	0.3%
19344936	1	0.3%
18797641	1	0.3%
16353543	1	0.3%
15318643	1	0.3%
14627061	1	0.3%
13507301	1	0.3%
13339952	1	0.3%
Other values (290)	290	96.7%

Value	Count	Frequency (%)
1993759	1	0.3%
1994857	1	0.3%
2002469	1	0.3%
2014724	1	0.3%
2018517	1	0.3%
2019272	1	0.3%
2022447	1	0.3%
2026378	1	0.3%
2032696	1	0.3%
2036769	1	0.3%

Value	Count	Frequency (%)
22572066	1	0.3%
21331409	1	0.3%
19344936	1	0.3%
18797641	1	0.3%
16353543	1	0.3%
15318643	1	0.3%
14627061	1	0.3%
13507301	1	0.3%
13339952	1	0.3%
13255807	1	0.3%

Cleaned_Headline
Text

Distinct	296
Distinct (%)	98.7%
Missing	0
Missing (%)	0.0%
Memory size	51.2 KiB

Length

Max length	239
Median length	120
Mean length	81.1
Min length	0

Characters and Unicode

Total characters	24330
Distinct characters	85
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	295 ?
Unique (%)	98.3%

Sample

1st row	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand
2nd row	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above
3rd row	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender
4th row	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently
5th row	McDonald's Removing 1 Breakfast Menu Item for Good on July 2

Value	Count	Frequency (%)
to	126	3.1%
the	99	2.4%
in	77	1.9%
for	66	1.6%
and	58	1.4%
of	54	1.3%
new	52	1.3%
a	50	1.2%
with	31	0.8%
is	30	0.7%
Other values (1768)	3408	84.1%

Most occurring characters

Value	Count	Frequency (%)
	3756	15.4%
e	2236	9.2%
a	1477	6.1%
o	1448	6.0%
t	1408	5.8%
r	1349	5.5%
n	1337	5.5%
i	1302	5.4%
s	1194	4.9%
l	844	3.5%
Other values (75)	7979	32.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	24330	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	3756	15.4%
e	2236	9.2%
a	1477	6.1%
o	1448	6.0%
t	1408	5.8%
r	1349	5.5%
n	1337	5.5%
i	1302	5.4%
s	1194	4.9%
l	844	3.5%
Other values (75)	7979	32.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	24330	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	3756	15.4%
e	2236	9.2%
a	1477	6.1%
o	1448	6.0%
t	1408	5.8%
r	1349	5.5%
n	1337	5.5%
i	1302	5.4%
s	1194	4.9%
l	844	3.5%
Other values (75)	7979	32.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	24330	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	3756	15.4%
e	2236	9.2%
a	1477	6.1%
o	1448	6.0%
t	1408	5.8%
r	1349	5.5%
n	1337	5.5%
i	1302	5.4%
s	1194	4.9%
l	844	3.5%
Other values (75)	7979	32.8%

Main_Category
Categorical

High correlation

Distinct	15
Distinct (%)	5.0%
Missing	0
Missing (%)	0.0%
Memory size	22.0 KiB

Finance_and_Business	65
News_and_Current_Events	65
Entertainment_and_Culture	30
Gastronomy	23
Health_and_Wellness	19
Other values (10)	98

Length

Max length	26
Median length	23
Mean length	17.57
Min length	0

Characters and Unicode

Total characters	5271
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.3%

Sample

1st row	Finance_and_Business
2nd row	News_and_Current_Events
3rd row	News_and_Current_Events
4th row	News_and_Current_Events
5th row	Gastronomy

Common Values

Value	Count	Frequency (%)
Finance_and_Business	65	21.7%
News_and_Current_Events	65	21.7%
Entertainment_and_Culture	30	10.0%
Gastronomy	23	7.7%
Health_and_Wellness	19	6.3%
Science	16	5.3%
Public_Safety	16	5.3%
Travel	14	4.7%
Education_and_Guides	12	4.0%
Sports	12	4.0%
Other values (5)	28	9.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
finance_and_business	65	22.0%
news_and_current_events	65	22.0%
entertainment_and_culture	30	10.2%
gastronomy	23	7.8%
health_and_wellness	19	6.4%
science	16	5.4%
public_safety	16	5.4%
travel	14	4.7%
education_and_guides	12	4.1%
sports	12	4.1%
Other values (4)	23	7.8%

Most occurring characters

Value	Count	Frequency (%)
n	706	13.4%
e	595	11.3%
_	497	9.4%
s	442	8.4%
a	395	7.5%
t	349	6.6%
i	255	4.8%
r	246	4.7%
u	237	4.5%
d	232	4.4%
Other values (25)	1317	25.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5271	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	706	13.4%
e	595	11.3%
_	497	9.4%
s	442	8.4%
a	395	7.5%
t	349	6.6%
i	255	4.8%
r	246	4.7%
u	237	4.5%
d	232	4.4%
Other values (25)	1317	25.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5271	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	706	13.4%
e	595	11.3%
_	497	9.4%
s	442	8.4%
a	395	7.5%
t	349	6.6%
i	255	4.8%
r	246	4.7%
u	237	4.5%
d	232	4.4%
Other values (25)	1317	25.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5271	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	706	13.4%
e	595	11.3%
_	497	9.4%
s	442	8.4%
a	395	7.5%
t	349	6.6%
i	255	4.8%
r	246	4.7%
u	237	4.5%
d	232	4.4%
Other values (25)	1317	25.0%

Subcategory_1
Text

Distinct	72
Distinct (%)	24.0%
Missing	0
Missing (%)	0.0%
Memory size	21.7 KiB

Length

Max length	30
Median length	22
Mean length	16.496667
Min length	0

Characters and Unicode

Total characters	4949
Distinct characters	50
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	29 ?
Unique (%)	9.7%

Sample

1st row	Companies & Entrepreneurship
2nd row	Politics
3rd row	Politics
4th row	Politics
5th row	Restaurants & Chefs

Value	Count	Frequency (%)
	101	18.2%
companies	27	4.9%
entrepreneurship	27	4.9%
politics	19	3.4%
celebrities	16	2.9%
influencers	16	2.9%
prevention	13	2.3%
alerts	12	2.2%
recipes	12	2.2%
companies_and_entrepreneurship	12	2.2%
Other values (92)	299	54.0%

Most occurring characters

Value	Count	Frequency (%)
e	540	10.9%
i	457	9.2%
n	454	9.2%
r	346	7.0%
s	325	6.6%
t	298	6.0%
a	278	5.6%
	259	5.2%
o	228	4.6%
l	205	4.1%
Other values (40)	1559	31.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	4949	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	540	10.9%
i	457	9.2%
n	454	9.2%
r	346	7.0%
s	325	6.6%
t	298	6.0%
a	278	5.6%
	259	5.2%
o	228	4.6%
l	205	4.1%
Other values (40)	1559	31.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	4949	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	540	10.9%
i	457	9.2%
n	454	9.2%
r	346	7.0%
s	325	6.6%
t	298	6.0%
a	278	5.6%
	259	5.2%
o	228	4.6%
l	205	4.1%
Other values (40)	1559	31.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	4949	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	540	10.9%
i	457	9.2%
n	454	9.2%
r	346	7.0%
s	325	6.6%
t	298	6.0%
a	278	5.6%
	259	5.2%
o	228	4.6%
l	205	4.1%
Other values (40)	1559	31.5%

Subcategory_2
Categorical

High correlation Imbalance

Distinct	34
Distinct (%)	11.3%
Missing	0
Missing (%)	0.0%
Memory size	18.3 KiB

N/A	211
Government	16
Tips	11
Advances	7
National	6
Other values (29)	49

Length

Max length	25
Median length	3
Mean length	5.0233333
Min length	0

Characters and Unicode

Total characters	1507
Distinct characters	42
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	17 ?
Unique (%)	5.7%

Sample

1st row	N/A
2nd row	Government
3rd row	Government
4th row	Government
5th row	N/A

Common Values

Value	Count	Frequency (%)
N/A	211	70.3%
Government	16	5.3%
Tips	11	3.7%
Advances	7	2.3%
National	6	2.0%
	5	1.7%
Desserts	4	1.3%
Main Courses	3	1.0%
Alerts & Prevention	3	1.0%
Analysis	3	1.0%
Other values (24)	31	10.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
n/a	211	66.4%
government	16	5.0%
tips	11	3.5%
advances	7	2.2%
	7	2.2%
national	6	1.9%
prevention	5	1.6%
desserts	4	1.3%
main	3	0.9%
alerts	3	0.9%
Other values (33)	45	14.2%

Most occurring characters

Value	Count	Frequency (%)
A	224	14.9%
N	217	14.4%
/	212	14.1%
e	123	8.2%
n	93	6.2%
s	82	5.4%
i	63	4.2%
t	58	3.8%
r	55	3.6%
o	45	3.0%
Other values (32)	335	22.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1507	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
A	224	14.9%
N	217	14.4%
/	212	14.1%
e	123	8.2%
n	93	6.2%
s	82	5.4%
i	63	4.2%
t	58	3.8%
r	55	3.6%
o	45	3.0%
Other values (32)	335	22.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1507	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
A	224	14.9%
N	217	14.4%
/	212	14.1%
e	123	8.2%
n	93	6.2%
s	82	5.4%
i	63	4.2%
t	58	3.8%
r	55	3.6%
o	45	3.0%
Other values (32)	335	22.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1507	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
A	224	14.9%
N	217	14.4%
/	212	14.1%
e	123	8.2%
n	93	6.2%
s	82	5.4%
i	63	4.2%
t	58	3.8%
r	55	3.6%
o	45	3.0%
Other values (32)	335	22.2%

Clarity_and_Conciseness_Value
Categorical

High correlation Imbalance

Distinct	4
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	18.0 KiB

High	291
	5
Medium	3
Low	1

Length

Max length	6
Median length	4
Mean length	3.95
Min length	0

Characters and Unicode

Total characters	1185
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.3%

Sample

1st row	High
2nd row	High
3rd row	High
4th row	High
5th row	High

Common Values

Value	Count	Frequency (%)
High	291	97.0%
	5	1.7%
Medium	3	1.0%
Low	1	0.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
high	291	98.6%
medium	3	1.0%
low	1	0.3%

Most occurring characters

Value	Count	Frequency (%)
i	294	24.8%
H	291	24.6%
g	291	24.6%
h	291	24.6%
M	3	0.3%
e	3	0.3%
d	3	0.3%
u	3	0.3%
m	3	0.3%
L	1	0.1%
Other values (2)	2	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1185	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	294	24.8%
H	291	24.6%
g	291	24.6%
h	291	24.6%
M	3	0.3%
e	3	0.3%
d	3	0.3%
u	3	0.3%
m	3	0.3%
L	1	0.1%
Other values (2)	2	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1185	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	294	24.8%
H	291	24.6%
g	291	24.6%
h	291	24.6%
M	3	0.3%
e	3	0.3%
d	3	0.3%
u	3	0.3%
m	3	0.3%
L	1	0.1%
Other values (2)	2	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1185	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	294	24.8%
H	291	24.6%
g	291	24.6%
h	291	24.6%
M	3	0.3%
e	3	0.3%
d	3	0.3%
u	3	0.3%
m	3	0.3%
L	1	0.1%
Other values (2)	2	0.2%

Clarity_and_Conciseness_Comment
Text

Distinct	275
Distinct (%)	91.7%
Missing	0
Missing (%)	0.0%
Memory size	43.8 KiB

Length

Max length	205
Median length	131
Mean length	91.91
Min length	0

Characters and Unicode

Total characters	27573
Distinct characters	70
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	267 ?
Unique (%)	89.0%

Sample

1st row	The main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.
2nd row	The main message is exceptionally clear, detailing who, what, and when without ambiguity.
3rd row	The main message is very clear and direct, stating precisely what occurred and why.
4th row	The main message is very clear and direct, stating the change, its source, and its impact.
5th row	The message is clear and to the point, identifying the brand, action, and date.

Value	Count	Frequency (%)
the	519	11.7%
and	354	8.0%
is	264	6.0%
clear	194	4.4%
message	190	4.3%
main	171	3.9%
to	156	3.5%
easy	124	2.8%
understand	120	2.7%
very	101	2.3%
Other values (776)	2231	50.4%

Most occurring characters

Value	Count	Frequency (%)
	4129	15.0%
e	3094	11.2%
a	2300	8.3%
t	1850	6.7%
n	1803	6.5%
s	1776	6.4%
i	1752	6.4%
r	1227	4.5%
d	1103	4.0%
c	1003	3.6%
Other values (60)	7536	27.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	27573	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	4129	15.0%
e	3094	11.2%
a	2300	8.3%
t	1850	6.7%
n	1803	6.5%
s	1776	6.4%
i	1752	6.4%
r	1227	4.5%
d	1103	4.0%
c	1003	3.6%
Other values (60)	7536	27.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	27573	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	4129	15.0%
e	3094	11.2%
a	2300	8.3%
t	1850	6.7%
n	1803	6.5%
s	1776	6.4%
i	1752	6.4%
r	1227	4.5%
d	1103	4.0%
c	1003	3.6%
Other values (60)	7536	27.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	27573	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	4129	15.0%
e	3094	11.2%
a	2300	8.3%
t	1850	6.7%
n	1803	6.5%
s	1776	6.4%
i	1752	6.4%
r	1227	4.5%
d	1103	4.0%
c	1003	3.6%
Other values (60)	7536	27.3%

Relevance_and_Timeliness_Value
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	18.0 KiB

High	286
Medium	9
	5

Length

Max length	6
Median length	4
Mean length	3.9933333
Min length	0

Characters and Unicode

Total characters	1198
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	High
2nd row	High
3rd row	High
4th row	High
5th row	High

Common Values

Value	Count	Frequency (%)
High	286	95.3%
Medium	9	3.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
high	286	96.9%
medium	9	3.1%

Most occurring characters

Value	Count	Frequency (%)
i	295	24.6%
H	286	23.9%
g	286	23.9%
h	286	23.9%
M	9	0.8%
e	9	0.8%
d	9	0.8%
u	9	0.8%
m	9	0.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1198	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	295	24.6%
H	286	23.9%
g	286	23.9%
h	286	23.9%
M	9	0.8%
e	9	0.8%
d	9	0.8%
u	9	0.8%
m	9	0.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1198	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	295	24.6%
H	286	23.9%
g	286	23.9%
h	286	23.9%
M	9	0.8%
e	9	0.8%
d	9	0.8%
u	9	0.8%
m	9	0.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1198	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	295	24.6%
H	286	23.9%
g	286	23.9%
h	286	23.9%
M	9	0.8%
e	9	0.8%
d	9	0.8%
u	9	0.8%
m	9	0.8%

Relevance_and_Timeliness_Comment
Text

Distinct	296
Distinct (%)	98.7%
Missing	0
Missing (%)	0.0%
Memory size	50.9 KiB

Length

Max length	233
Median length	150.5
Mean length	115.74667
Min length	0

Characters and Unicode

Total characters	34724
Distinct characters	72
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	295 ?
Unique (%)	98.3%

Sample

1st row	The headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.
2nd row	Highly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.
3rd row	The topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.
4th row	Highly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.
5th row	Highly relevant for McDonald's customers and tied to a specific future date, July 2.

Value	Count	Frequency (%)
and	360	7.0%
a	206	4.0%
relevant	183	3.6%
to	148	2.9%
highly	140	2.7%
the	128	2.5%
of	121	2.4%
interest	118	2.3%
are	118	2.3%
is	104	2.0%
Other values (1033)	3486	68.2%

Most occurring characters

Value	Count	Frequency (%)
	4817	13.9%
e	3779	10.9%
n	2552	7.3%
i	2523	7.3%
a	2429	7.0%
t	2231	6.4%
r	1915	5.5%
s	1785	5.1%
o	1633	4.7%
l	1505	4.3%
Other values (62)	9555	27.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	34724	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	4817	13.9%
e	3779	10.9%
n	2552	7.3%
i	2523	7.3%
a	2429	7.0%
t	2231	6.4%
r	1915	5.5%
s	1785	5.1%
o	1633	4.7%
l	1505	4.3%
Other values (62)	9555	27.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	34724	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	4817	13.9%
e	3779	10.9%
n	2552	7.3%
i	2523	7.3%
a	2429	7.0%
t	2231	6.4%
r	1915	5.5%
s	1785	5.1%
o	1633	4.7%
l	1505	4.3%
Other values (62)	9555	27.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	34724	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	4817	13.9%
e	3779	10.9%
n	2552	7.3%
i	2523	7.3%
a	2429	7.0%
t	2231	6.4%
r	1915	5.5%
s	1785	5.1%
o	1633	4.7%
l	1505	4.3%
Other values (62)	9555	27.5%

Strategic_Keyword_Usage_Value
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	18.0 KiB

High	289
Medium	6
	5

Length

Max length	6
Median length	4
Mean length	3.9733333
Min length	0

Characters and Unicode

Total characters	1192
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	High
2nd row	High
3rd row	High
4th row	High
5th row	High

Common Values

Value	Count	Frequency (%)
High	289	96.3%
Medium	6	2.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
high	289	98.0%
medium	6	2.0%

Most occurring characters

Value	Count	Frequency (%)
i	295	24.7%
H	289	24.2%
g	289	24.2%
h	289	24.2%
M	6	0.5%
e	6	0.5%
d	6	0.5%
u	6	0.5%
m	6	0.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1192	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	295	24.7%
H	289	24.2%
g	289	24.2%
h	289	24.2%
M	6	0.5%
e	6	0.5%
d	6	0.5%
u	6	0.5%
m	6	0.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1192	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	295	24.7%
H	289	24.2%
g	289	24.2%
h	289	24.2%
M	6	0.5%
e	6	0.5%
d	6	0.5%
u	6	0.5%
m	6	0.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1192	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	295	24.7%
H	289	24.2%
g	289	24.2%
h	289	24.2%
M	6	0.5%
e	6	0.5%
d	6	0.5%
u	6	0.5%
m	6	0.5%

Strategic_Keyword_Usage_Comment
Text

Distinct	296
Distinct (%)	98.7%
Missing	0
Missing (%)	0.0%
Memory size	57.0 KiB

Length

Max length	229
Median length	160
Mean length	129.62667
Min length	0

Characters and Unicode

Total characters	38888
Distinct characters	80
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	295 ?
Unique (%)	98.3%

Sample

1st row	Key terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.
2nd row	Contains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.
3rd row	Uses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.
4th row	Uses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.
5th row	Uses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.

Value	Count	Frequency (%)
and	462	8.4%
keywords	252	4.6%
like	237	4.3%
are	233	4.2%
relevant	212	3.9%
highly	188	3.4%
to	155	2.8%
uses	143	2.6%
the	122	2.2%
which	106	1.9%
Other values (1353)	3376	61.5%

Most occurring characters

Value	Count	Frequency (%)
	5191	13.3%
e	4088	10.5%
a	2689	6.9%
r	2239	5.8%
t	2049	5.3%
n	2044	5.3%
i	1999	5.1%
s	1930	5.0%
l	1640	4.2%
o	1548	4.0%
Other values (70)	13471	34.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	38888	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	5191	13.3%
e	4088	10.5%
a	2689	6.9%
r	2239	5.8%
t	2049	5.3%
n	2044	5.3%
i	1999	5.1%
s	1930	5.0%
l	1640	4.2%
o	1548	4.0%
Other values (70)	13471	34.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	38888	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	5191	13.3%
e	4088	10.5%
a	2689	6.9%
r	2239	5.8%
t	2049	5.3%
n	2044	5.3%
i	1999	5.1%
s	1930	5.0%
l	1640	4.2%
o	1548	4.0%
Other values (70)	13471	34.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	38888	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	5191	13.3%
e	4088	10.5%
a	2689	6.9%
r	2239	5.8%
t	2049	5.3%
n	2044	5.3%
i	1999	5.1%
s	1930	5.0%
l	1640	4.2%
o	1548	4.0%
Other values (70)	13471	34.6%

Originality_and_Differentiation_Value
Categorical

High correlation

Distinct	4
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	18.4 KiB

Medium	210
High	69
Low	16
	5

Length

Max length	6
Median length	6
Mean length	5.28
Min length	0

Characters and Unicode

Total characters	1584
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Medium
2nd row	Medium
3rd row	Medium
4th row	Medium
5th row	Medium

Common Values

Value	Count	Frequency (%)
Medium	210	70.0%
High	69	23.0%
Low	16	5.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
medium	210	71.2%
high	69	23.4%
low	16	5.4%

Most occurring characters

Value	Count	Frequency (%)
i	279	17.6%
M	210	13.3%
e	210	13.3%
d	210	13.3%
u	210	13.3%
m	210	13.3%
H	69	4.4%
g	69	4.4%
h	69	4.4%
L	16	1.0%
Other values (2)	32	2.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1584	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
i	279	17.6%
M	210	13.3%
e	210	13.3%
d	210	13.3%
u	210	13.3%
m	210	13.3%
H	69	4.4%
g	69	4.4%
h	69	4.4%
L	16	1.0%
Other values (2)	32	2.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1584	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
i	279	17.6%
M	210	13.3%
e	210	13.3%
d	210	13.3%
u	210	13.3%
m	210	13.3%
H	69	4.4%
g	69	4.4%
h	69	4.4%
L	16	1.0%
Other values (2)	32	2.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1584	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
i	279	17.6%
M	210	13.3%
e	210	13.3%
d	210	13.3%
u	210	13.3%
m	210	13.3%
H	69	4.4%
g	69	4.4%
h	69	4.4%
L	16	1.0%
Other values (2)	32	2.0%

Originality_and_Differentiation_Comment
Text

Distinct	296
Distinct (%)	98.7%
Missing	0
Missing (%)	0.0%
Memory size	57.0 KiB

Length

Max length	255
Median length	165
Mean length	131.35667
Min length	0

Characters and Unicode

Total characters	39407
Distinct characters	83
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	295 ?
Unique (%)	98.3%

Sample

1st row	While the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.
2nd row	While a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.
3rd row	The specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.
4th row	While the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.
5th row	While common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.

Value	Count	Frequency (%)
the	487	8.0%
a	334	5.5%
is	221	3.6%
and	210	3.4%
unique	161	2.6%
specific	158	2.6%
of	148	2.4%
common	145	2.4%
while	143	2.3%
it	116	1.9%
Other values (1215)	3968	65.1%

Most occurring characters

Value	Count	Frequency (%)
	5796	14.7%
e	3845	9.8%
i	3148	8.0%
n	2637	6.7%
t	2582	6.6%
a	2529	6.4%
o	1977	5.0%
s	1901	4.8%
r	1697	4.3%
c	1378	3.5%
Other values (73)	11917	30.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	39407	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	5796	14.7%
e	3845	9.8%
i	3148	8.0%
n	2637	6.7%
t	2582	6.6%
a	2529	6.4%
o	1977	5.0%
s	1901	4.8%
r	1697	4.3%
c	1378	3.5%
Other values (73)	11917	30.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	39407	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	5796	14.7%
e	3845	9.8%
i	3148	8.0%
n	2637	6.7%
t	2582	6.6%
a	2529	6.4%
o	1977	5.0%
s	1901	4.8%
r	1697	4.3%
c	1378	3.5%
Other values (73)	11917	30.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	39407	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	5796	14.7%
e	3845	9.8%
i	3148	8.0%
n	2637	6.7%
t	2582	6.6%
a	2529	6.4%
o	1977	5.0%
s	1901	4.8%
r	1697	4.3%
c	1378	3.5%
Other values (73)	11917	30.2%

Contains_Numbers
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	151
Yes	144
	5

Length

Max length	3
Median length	2
Mean length	2.4466667
Min length	0

Characters and Unicode

Total characters	734
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
No	151	50.3%
Yes	144	48.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	151	51.2%
yes	144	48.8%

Most occurring characters

Value	Count	Frequency (%)
N	151	20.6%
o	151	20.6%
Y	144	19.6%
e	144	19.6%
s	144	19.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	734	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	151	20.6%
o	151	20.6%
Y	144	19.6%
e	144	19.6%
s	144	19.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	734	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	151	20.6%
o	151	20.6%
Y	144	19.6%
e	144	19.6%
s	144	19.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	734	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	151	20.6%
o	151	20.6%
Y	144	19.6%
e	144	19.6%
s	144	19.6%

Contains_Quotes
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	255
Yes	40
	5

Length

Max length	3
Median length	2
Mean length	2.1
Min length	0

Characters and Unicode

Total characters	630
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	255	85.0%
Yes	40	13.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	255	86.4%
yes	40	13.6%

Most occurring characters

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	630	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	630	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	630	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Contains_Question_Mark
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	283
Yes	12
	5

Length

Max length	3
Median length	2
Mean length	2.0066667
Min length	0

Characters and Unicode

Total characters	602
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	283	94.3%
Yes	12	4.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	283	95.9%
yes	12	4.1%

Most occurring characters

Value	Count	Frequency (%)
N	283	47.0%
o	283	47.0%
Y	12	2.0%
e	12	2.0%
s	12	2.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	602	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	283	47.0%
o	283	47.0%
Y	12	2.0%
e	12	2.0%
s	12	2.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	602	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	283	47.0%
o	283	47.0%
Y	12	2.0%
e	12	2.0%
s	12	2.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	602	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	283	47.0%
o	283	47.0%
Y	12	2.0%
e	12	2.0%
s	12	2.0%

Contains_Colon
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	255
Yes	40
	5

Length

Max length	3
Median length	2
Mean length	2.1
Min length	0

Characters and Unicode

Total characters	630
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	255	85.0%
Yes	40	13.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	255	86.4%
yes	40	13.6%

Most occurring characters

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	630	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	630	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	630	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	255	40.5%
o	255	40.5%
Y	40	6.3%
e	40	6.3%
s	40	6.3%

Contains_Hyphen
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	228
Yes	67
	5

Length

Max length	3
Median length	2
Mean length	2.19
Min length	0

Characters and Unicode

Total characters	657
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	228	76.0%
Yes	67	22.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	228	77.3%
yes	67	22.7%

Most occurring characters

Value	Count	Frequency (%)
N	228	34.7%
o	228	34.7%
Y	67	10.2%
e	67	10.2%
s	67	10.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	657	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	228	34.7%
o	228	34.7%
Y	67	10.2%
e	67	10.2%
s	67	10.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	657	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	228	34.7%
o	228	34.7%
Y	67	10.2%
e	67	10.2%
s	67	10.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	657	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	228	34.7%
o	228	34.7%
Y	67	10.2%
e	67	10.2%
s	67	10.2%

Contains_Exclamation_Mark
Categorical

High correlation Imbalance

Distinct	2
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	295
	5

Length

Max length	2
Median length	2
Mean length	1.9666667
Min length	0

Characters and Unicode

Total characters	590
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	295	98.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	295	100.0%

Most occurring characters

Value	Count	Frequency (%)
N	295	50.0%
o	295	50.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	590	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	295	50.0%
o	295	50.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	590	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	295	50.0%
o	295	50.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	590	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	295	50.0%
o	295	50.0%

Starts_With_Number
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	274
Yes	21
	5

Length

Max length	3
Median length	2
Mean length	2.0366667
Min length	0

Characters and Unicode

Total characters	611
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	274	91.3%
Yes	21	7.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	274	92.9%
yes	21	7.1%

Most occurring characters

Value	Count	Frequency (%)
N	274	44.8%
o	274	44.8%
Y	21	3.4%
e	21	3.4%
s	21	3.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	611	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	274	44.8%
o	274	44.8%
Y	21	3.4%
e	21	3.4%
s	21	3.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	611	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	274	44.8%
o	274	44.8%
Y	21	3.4%
e	21	3.4%
s	21	3.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	611	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	274	44.8%
o	274	44.8%
Y	21	3.4%
e	21	3.4%
s	21	3.4%

Ends_With_Question_Mark
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	285
Yes	10
	5

Length

Max length	3
Median length	2
Mean length	2
Min length	0

Characters and Unicode

Total characters	600
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	285	95.0%
Yes	10	3.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	285	96.6%
yes	10	3.4%

Most occurring characters

Value	Count	Frequency (%)
N	285	47.5%
o	285	47.5%
Y	10	1.7%
e	10	1.7%
s	10	1.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	600	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	285	47.5%
o	285	47.5%
Y	10	1.7%
e	10	1.7%
s	10	1.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	600	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	285	47.5%
o	285	47.5%
Y	10	1.7%
e	10	1.7%
s	10	1.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	600	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	285	47.5%
o	285	47.5%
Y	10	1.7%
e	10	1.7%
s	10	1.7%

Length_General_Assessment
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	19.5 KiB

Adequate	277
Too long, risk of truncation	18
	5

Length

Max length	28
Median length	8
Mean length	9.0666667
Min length	0

Characters and Unicode

Total characters	2720
Distinct characters	20
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Adequate
2nd row	Adequate
3rd row	Adequate
4th row	Adequate
5th row	Adequate

Common Values

Value	Count	Frequency (%)
Adequate	277	92.3%
Too long, risk of truncation	18	6.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
adequate	277	75.5%
too	18	4.9%
long	18	4.9%
risk	18	4.9%
of	18	4.9%
truncation	18	4.9%

Most occurring characters

Value	Count	Frequency (%)
e	554	20.4%
t	313	11.5%
u	295	10.8%
a	295	10.8%
q	277	10.2%
d	277	10.2%
A	277	10.2%
o	90	3.3%
	72	2.6%
n	54	2.0%
Other values (10)	216	7.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2720	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	554	20.4%
t	313	11.5%
u	295	10.8%
a	295	10.8%
q	277	10.2%
d	277	10.2%
A	277	10.2%
o	90	3.3%
	72	2.6%
n	54	2.0%
Other values (10)	216	7.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2720	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	554	20.4%
t	313	11.5%
u	295	10.8%
a	295	10.8%
q	277	10.2%
d	277	10.2%
A	277	10.2%
o	90	3.3%
	72	2.6%
n	54	2.0%
Other values (10)	216	7.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2720	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	554	20.4%
t	313	11.5%
u	295	10.8%
a	295	10.8%
q	277	10.2%
d	277	10.2%
A	277	10.2%
o	90	3.3%
	72	2.6%
n	54	2.0%
Other values (10)	216	7.9%

Length_Number_of_Characters
Text

Distinct	90
Distinct (%)	30.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

Length

Max length	3
Median length	2
Mean length	2.1433333
Min length	0

Characters and Unicode

Total characters	643
Distinct characters	10
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	33 ?
Unique (%)	11.0%

Sample

1st row	105
2nd row	101
3rd row	106
4th row	173
5th row	57

Value	Count	Frequency (%)
50	14	4.7%
59	13	4.4%
90	11	3.7%
60	11	3.7%
69	10	3.4%
70	9	3.1%
73	8	2.7%
64	8	2.7%
62	8	2.7%
67	7	2.4%
Other values (79)	196	66.4%

Most occurring characters

Value	Count	Frequency (%)
5	97	15.1%
1	84	13.1%
9	79	12.3%
6	78	12.1%
0	75	11.7%
7	67	10.4%
4	55	8.6%
8	43	6.7%
3	36	5.6%
2	29	4.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
5	97	15.1%
1	84	13.1%
9	79	12.3%
6	78	12.1%
0	75	11.7%
7	67	10.4%
4	55	8.6%
8	43	6.7%
3	36	5.6%
2	29	4.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
5	97	15.1%
1	84	13.1%
9	79	12.3%
6	78	12.1%
0	75	11.7%
7	67	10.4%
4	55	8.6%
8	43	6.7%
3	36	5.6%
2	29	4.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
5	97	15.1%
1	84	13.1%
9	79	12.3%
6	78	12.1%
0	75	11.7%
7	67	10.4%
4	55	8.6%
8	43	6.7%
3	36	5.6%
2	29	4.5%

Emphatic_Capitalization_Usage
Categorical

High correlation Imbalance

Distinct	17
Distinct (%)	5.7%
Missing	0
Missing (%)	0.0%
Memory size	18.2 KiB

No	280
	5
Yes, "The Show Must Go On", justified use as it is a direct quote.	1
Yes, 'DWTS', 'SYTYCD', justified use	1
Yes, 'Already Made A Payment', justified use within a quote to emphasize the person's statement.	1
Other values (12)	12

Length

Max length	112
Median length	2
Mean length	4.6766667
Min length	0

Characters and Unicode

Total characters	1403
Distinct characters	53
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15 ?
Unique (%)	5.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	280	93.3%
	5	1.7%
Yes, "The Show Must Go On", justified use as it is a direct quote.	1	0.3%
Yes, 'DWTS', 'SYTYCD', justified use	1	0.3%
Yes, 'Already Made A Payment', justified use within a quote to emphasize the person's statement.	1	0.3%
Yes, '11 Phrases Deeply Unhappy People Use On A Regular Basis', seeks impact	1	0.3%
Yes, 'No Touch', seeks impact	1	0.3%
Yes, 'EARLIER', seeks impact	1	0.3%
Yes, 'NY', justified use	1	0.3%
Yes, 'Best', 'All Time', seeks impact	1	0.3%
Other values (7)	7	2.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
no	281	69.6%
yes	15	3.7%
impact	9	2.2%
seeks	9	2.2%
use	7	1.7%
justified	6	1.5%
a	4	1.0%
the	4	1.0%
and	3	0.7%
quote	2	0.5%
Other values (62)	64	15.8%

Most occurring characters

Value	Count	Frequency (%)
o	302	21.5%
N	283	20.2%
	109	7.8%
e	97	6.9%
s	69	4.9%
'	55	3.9%
,	46	3.3%
i	45	3.2%
t	39	2.8%
a	37	2.6%
Other values (43)	321	22.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1403	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
o	302	21.5%
N	283	20.2%
	109	7.8%
e	97	6.9%
s	69	4.9%
'	55	3.9%
,	46	3.3%
i	45	3.2%
t	39	2.8%
a	37	2.6%
Other values (43)	321	22.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1403	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
o	302	21.5%
N	283	20.2%
	109	7.8%
e	97	6.9%
s	69	4.9%
'	55	3.9%
,	46	3.3%
i	45	3.2%
t	39	2.8%
a	37	2.6%
Other values (43)	321	22.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1403	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
o	302	21.5%
N	283	20.2%
	109	7.8%
e	97	6.9%
s	69	4.9%
'	55	3.9%
,	46	3.3%
i	45	3.2%
t	39	2.8%
a	37	2.6%
Other values (43)	321	22.9%

Main_Classification
Categorical

High correlation

Distinct	21
Distinct (%)	7.0%
Missing	0
Missing (%)	0.0%
Memory size	22.3 KiB

Declarative Simple	186
Mystery/Revelation	24
Attribution ('according to', 'reveals')	18
Direct Question	12
List/Numbered	10
Other values (16)	50

Length

Max length	52
Median length	18
Mean length	18.833333
Min length	0

Characters and Unicode

Total characters	5650
Distinct characters	39
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8 ?
Unique (%)	2.7%

Sample

1st row	Declarative Simple
2nd row	Declarative Simple
3rd row	Declarative Simple
4th row	Urgency
5th row	Declarative Simple

Common Values

Value	Count	Frequency (%)
Declarative Simple	186	62.0%
Mystery/Revelation	24	8.0%
Attribution ('according to', 'reveals')	18	6.0%
Direct Question	12	4.0%
List/Numbered	10	3.3%
List/Numbered ('5 ways')	9	3.0%
Direct Quote	8	2.7%
	5	1.7%
Comparative	5	1.7%
Urgency	4	1.3%
Other values (11)	19	6.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
declarative	187	31.3%
simple	186	31.2%
mystery/revelation	28	4.7%
direct	22	3.7%
attribution	19	3.2%
to	19	3.2%
according	19	3.2%
reveals	19	3.2%
list/numbered	19	3.2%
question	14	2.3%
Other values (17)	65	10.9%

Most occurring characters

Value	Count	Frequency (%)
e	807	14.3%
i	536	9.5%
a	469	8.3%
l	429	7.6%
t	426	7.5%
r	347	6.1%
	302	5.3%
c	258	4.6%
v	248	4.4%
m	212	3.8%
Other values (29)	1616	28.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	5650	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	807	14.3%
i	536	9.5%
a	469	8.3%
l	429	7.6%
t	426	7.5%
r	347	6.1%
	302	5.3%
c	258	4.6%
v	248	4.4%
m	212	3.8%
Other values (29)	1616	28.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	5650	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	807	14.3%
i	536	9.5%
a	469	8.3%
l	429	7.6%
t	426	7.5%
r	347	6.1%
	302	5.3%
c	258	4.6%
v	248	4.4%
m	212	3.8%
Other values (29)	1616	28.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	5650	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	807	14.3%
i	536	9.5%
a	469	8.3%
l	429	7.6%
t	426	7.5%
r	347	6.1%
	302	5.3%
c	258	4.6%
v	248	4.4%
m	212	3.8%
Other values (29)	1616	28.6%

Comment_Type_Justification
Text

Distinct	296
Distinct (%)	98.7%
Missing	0
Missing (%)	0.0%
Memory size	55.0 KiB

Length

Max length	305
Median length	161.5
Mean length	128.79
Min length	0

Characters and Unicode

Total characters	38637
Distinct characters	74
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	295 ?
Unique (%)	98.3%

Sample

1st row	The headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.
2nd row	The headline directly states a new rule and its implications, serving as a straightforward announcement.
3rd row	The headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.
4th row	The headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.
5th row	The headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.

Value	Count	Frequency (%)
a	550	9.5%
the	467	8.0%
headline	272	4.7%
or	209	3.6%
without	153	2.6%
statement	118	2.0%
question	117	2.0%
and	110	1.9%
an	110	1.9%
about	108	1.9%
Other values (847)	3598	61.9%

Most occurring characters

Value	Count	Frequency (%)
	5517	14.3%
e	3899	10.1%
t	3322	8.6%
a	2838	7.3%
i	2813	7.3%
n	2523	6.5%
s	1952	5.1%
o	1939	5.0%
r	1805	4.7%
h	1274	3.3%
Other values (64)	10755	27.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	38637	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	5517	14.3%
e	3899	10.1%
t	3322	8.6%
a	2838	7.3%
i	2813	7.3%
n	2523	6.5%
s	1952	5.1%
o	1939	5.0%
r	1805	4.7%
h	1274	3.3%
Other values (64)	10755	27.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	38637	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	5517	14.3%
e	3899	10.1%
t	3322	8.6%
a	2838	7.3%
i	2813	7.3%
n	2523	6.5%
s	1952	5.1%
o	1939	5.0%
r	1805	4.7%
h	1274	3.3%
Other values (64)	10755	27.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	38637	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	5517	14.3%
e	3899	10.1%
t	3322	8.6%
a	2838	7.3%
i	2813	7.3%
n	2523	6.5%
s	1952	5.1%
o	1939	5.0%
r	1805	4.7%
h	1274	3.3%
Other values (64)	10755	27.8%

Temporal_Urgency_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	250
Yes	45
	5

Length

Max length	3
Median length	2
Mean length	2.1166667
Min length	0

Characters and Unicode

Total characters	635
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	Yes
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	250	83.3%
Yes	45	15.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	250	84.7%
yes	45	15.3%

Most occurring characters

Value	Count	Frequency (%)
N	250	39.4%
o	250	39.4%
Y	45	7.1%
e	45	7.1%
s	45	7.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	635	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	250	39.4%
o	250	39.4%
Y	45	7.1%
e	45	7.1%
s	45	7.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	635	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	250	39.4%
o	250	39.4%
Y	45	7.1%
e	45	7.1%
s	45	7.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	635	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	250	39.4%
o	250	39.4%
Y	45	7.1%
e	45	7.1%
s	45	7.1%

Temporal_Urgency_Words
Categorical

High correlation Imbalance

Distinct	36
Distinct (%)	12.0%
Missing	0
Missing (%)	0.0%
Memory size	17.9 KiB

[]	250
	5
["Just"]	4
["Now"]	3
["abruptly"]	3
Other values (31)	35

Length

Max length	27
Median length	2
Mean length	3.7466667
Min length	0

Characters and Unicode

Total characters	1124
Distinct characters	50
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	27 ?
Unique (%)	9.0%

Sample

1st row	["immediately"]
2nd row	["Begins July 2025"]
3rd row	[]
4th row	["immediate","urgently"]
5th row	[]

Common Values

Value	Count	Frequency (%)
[]	250	83.3%
	5	1.7%
["Just"]	4	1.3%
["Now"]	3	1.0%
["abruptly"]	3	1.0%
["Already"]	2	0.7%
["immediately"]	2	0.7%
["suddenly"]	2	0.7%
["This Month"]	2	0.7%
["Begins July 2025"]	1	0.3%
Other values (26)	26	8.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	250	78.6%
this	8	2.5%
just	4	1.3%
now	3	0.9%
abruptly	3	0.9%
month	3	0.9%
years	3	0.9%
immediately	2	0.6%
suddenly	2	0.6%
july	2	0.6%
Other values (34)	38	11.9%

Most occurring characters

Value	Count	Frequency (%)
[	295	26.2%
]	295	26.2%
"	106	9.4%
e	42	3.7%
t	30	2.7%
a	26	2.3%
i	26	2.3%
s	23	2.0%
	23	2.0%
l	22	2.0%
Other values (40)	236	21.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1124	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	295	26.2%
]	295	26.2%
"	106	9.4%
e	42	3.7%
t	30	2.7%
a	26	2.3%
i	26	2.3%
s	23	2.0%
	23	2.0%
l	22	2.0%
Other values (40)	236	21.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1124	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	295	26.2%
]	295	26.2%
"	106	9.4%
e	42	3.7%
t	30	2.7%
a	26	2.3%
i	26	2.3%
s	23	2.0%
	23	2.0%
l	22	2.0%
Other values (40)	236	21.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1124	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	295	26.2%
]	295	26.2%
"	106	9.4%
e	42	3.7%
t	30	2.7%
a	26	2.3%
i	26	2.3%
s	23	2.0%
	23	2.0%
l	22	2.0%
Other values (40)	236	21.0%

Exclusivity_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	288
Yes	7
	5

Length

Max length	3
Median length	2
Mean length	1.99
Min length	0

Characters and Unicode

Total characters	597
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	288	96.0%
Yes	7	2.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	288	97.6%
yes	7	2.4%

Most occurring characters

Value	Count	Frequency (%)
N	288	48.2%
o	288	48.2%
Y	7	1.2%
e	7	1.2%
s	7	1.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	597	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	288	48.2%
o	288	48.2%
Y	7	1.2%
e	7	1.2%
s	7	1.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	597	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	288	48.2%
o	288	48.2%
Y	7	1.2%
e	7	1.2%
s	7	1.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	597	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	288	48.2%
o	288	48.2%
Y	7	1.2%
e	7	1.2%
s	7	1.2%

Exclusivity_Words
Categorical

High correlation Imbalance

Distinct	8
Distinct (%)	2.7%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

[]	288
	5
["Limited-Edition"]	2
["first"]	1
["first","never before seen"]	1
Other values (3)	3

Length

Max length	29
Median length	2
Mean length	2.29
Min length	0

Characters and Unicode

Total characters	687
Distinct characters	28
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	1.7%

Sample

1st row	[]
2nd row	[]
3rd row	[]
4th row	[]
5th row	[]

Common Values

Value	Count	Frequency (%)
[]	288	96.0%
	5	1.7%
["Limited-Edition"]	2	0.7%
["first"]	1	0.3%
["first","never before seen"]	1	0.3%
["only"]	1	0.3%
["unique"]	1	0.3%
["this US state"]	1	0.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
	288	96.3%
limited-edition	2	0.7%
first	1	0.3%
first","never	1	0.3%
before	1	0.3%
seen	1	0.3%
only	1	0.3%
unique	1	0.3%
this	1	0.3%
us	1	0.3%

Most occurring characters

Value	Count	Frequency (%)
[	295	42.9%
]	295	42.9%
"	16	2.3%
i	12	1.7%
e	10	1.5%
t	9	1.3%
n	6	0.9%
s	5	0.7%
	4	0.6%
r	4	0.6%
Other values (18)	31	4.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	687	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	295	42.9%
]	295	42.9%
"	16	2.3%
i	12	1.7%
e	10	1.5%
t	9	1.3%
n	6	0.9%
s	5	0.7%
	4	0.6%
r	4	0.6%
Other values (18)	31	4.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	687	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	295	42.9%
]	295	42.9%
"	16	2.3%
i	12	1.7%
e	10	1.5%
t	9	1.3%
n	6	0.9%
s	5	0.7%
	4	0.6%
r	4	0.6%
Other values (18)	31	4.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	687	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	295	42.9%
]	295	42.9%
"	16	2.3%
i	12	1.7%
e	10	1.5%
t	9	1.3%
n	6	0.9%
s	5	0.7%
	4	0.6%
r	4	0.6%
Other values (18)	31	4.5%

Authority_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	211
Yes	84
	5

Length

Max length	3
Median length	2
Mean length	2.2466667
Min length	0

Characters and Unicode

Total characters	674
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	211	70.3%
Yes	84	28.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	211	71.5%
yes	84	28.5%

Most occurring characters

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	674	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	674	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	674	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Authority_Words
Text

Distinct	76
Distinct (%)	25.3%
Missing	0
Missing (%)	0.0%
Memory size	18.7 KiB

Length

Max length	55
Median length	2
Mean length	6.3733333
Min length	0

Characters and Unicode

Total characters	1912
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	70 ?
Unique (%)	23.3%

Sample

1st row	["CEO admits"]
2nd row	[]
3rd row	[]
4th row	["DMV","confirms"]
5th row	[]

Value	Count	Frequency (%)
	211	63.4%
experts	6	1.8%
tsa	5	1.5%
law	3	0.9%
social	3	0.9%
confirms	2	0.6%
ceo	2	0.6%
to	2	0.6%
scientists	2	0.6%
out	2	0.6%
Other values (90)	95	28.5%

Most occurring characters

Value	Count	Frequency (%)
[	295	15.4%
]	295	15.4%
"	244	12.8%
e	96	5.0%
i	73	3.8%
s	68	3.6%
o	68	3.6%
r	64	3.3%
n	63	3.3%
t	62	3.2%
Other values (46)	584	30.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1912	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	295	15.4%
]	295	15.4%
"	244	12.8%
e	96	5.0%
i	73	3.8%
s	68	3.6%
o	68	3.6%
r	64	3.3%
n	63	3.3%
t	62	3.2%
Other values (46)	584	30.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1912	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	295	15.4%
]	295	15.4%
"	244	12.8%
e	96	5.0%
i	73	3.8%
s	68	3.6%
o	68	3.6%
r	64	3.3%
n	63	3.3%
t	62	3.2%
Other values (46)	584	30.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1912	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	295	15.4%
]	295	15.4%
"	244	12.8%
e	96	5.0%
i	73	3.8%
s	68	3.6%
o	68	3.6%
r	64	3.3%
n	63	3.3%
t	62	3.2%
Other values (46)	584	30.5%

Solution_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	244
Yes	51
	5

Length

Max length	3
Median length	2
Mean length	2.1366667
Min length	0

Characters and Unicode

Total characters	641
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	244	81.3%
Yes	51	17.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	244	82.7%
yes	51	17.3%

Most occurring characters

Value	Count	Frequency (%)
N	244	38.1%
o	244	38.1%
Y	51	8.0%
e	51	8.0%
s	51	8.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	641	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	244	38.1%
o	244	38.1%
Y	51	8.0%
e	51	8.0%
s	51	8.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	641	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	244	38.1%
o	244	38.1%
Y	51	8.0%
e	51	8.0%
s	51	8.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	641	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	244	38.1%
o	244	38.1%
Y	51	8.0%
e	51	8.0%
s	51	8.0%

Solution_Words
Categorical

High correlation Imbalance

Distinct	50
Distinct (%)	16.7%
Missing	0
Missing (%)	0.0%
Memory size	18.2 KiB

[]	244
	5
["How to"]	2
["How To Clean"]	2
["Restored"]	2
Other values (45)	45

Length

Max length	44
Median length	2
Mean length	4.5966667
Min length	0

Characters and Unicode

Total characters	1379
Distinct characters	53
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	45 ?
Unique (%)	15.0%

Sample

1st row	[]
2nd row	[]
3rd row	[]
4th row	[]
5th row	[]

Common Values

Value	Count	Frequency (%)
[]	244	81.3%
	5	1.7%
["How to"]	2	0.7%
["How To Clean"]	2	0.7%
["Restored"]	2	0.7%
["method","without effort"]	1	0.3%
["build","indestructible"]	1	0.3%
["Turn It Around","Real Estate Investments"]	1	0.3%
["enable new security features"]	1	0.3%
["Way"]	1	0.3%
Other values (40)	40	13.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	244	69.7%
how	10	2.9%
to	10	2.9%
it	3	0.9%
new	3	0.9%
way	3	0.9%
best	3	0.9%
restored	2	0.6%
use	2	0.6%
them	2	0.6%
Other values (67)	68	19.4%

Most occurring characters

Value	Count	Frequency (%)
[	295	21.4%
]	295	21.4%
"	134	9.7%
e	82	5.9%
	55	4.0%
t	54	3.9%
o	47	3.4%
s	35	2.5%
a	31	2.2%
r	30	2.2%
Other values (43)	321	23.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1379	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	295	21.4%
]	295	21.4%
"	134	9.7%
e	82	5.9%
	55	4.0%
t	54	3.9%
o	47	3.4%
s	35	2.5%
a	31	2.2%
r	30	2.2%
Other values (43)	321	23.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1379	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	295	21.4%
]	295	21.4%
"	134	9.7%
e	82	5.9%
	55	4.0%
t	54	3.9%
o	47	3.4%
s	35	2.5%
a	31	2.2%
r	30	2.2%
Other values (43)	321	23.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1379	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	295	21.4%
]	295	21.4%
"	134	9.7%
e	82	5.9%
	55	4.0%
t	54	3.9%
o	47	3.4%
s	35	2.5%
a	31	2.2%
r	30	2.2%
Other values (43)	321	23.3%

Economic_Benefit_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	253
Yes	42
	5

Length

Max length	3
Median length	2
Mean length	2.1066667
Min length	0

Characters and Unicode

Total characters	632
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	253	84.3%
Yes	42	14.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	253	85.8%
yes	42	14.2%

Most occurring characters

Value	Count	Frequency (%)
N	253	40.0%
o	253	40.0%
Y	42	6.6%
e	42	6.6%
s	42	6.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	632	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	253	40.0%
o	253	40.0%
Y	42	6.6%
e	42	6.6%
s	42	6.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	632	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	253	40.0%
o	253	40.0%
Y	42	6.6%
e	42	6.6%
s	42	6.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	632	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	253	40.0%
o	253	40.0%
Y	42	6.6%
e	42	6.6%
s	42	6.6%

Economic_Benefit_Words
Categorical

High correlation Imbalance

Distinct	42
Distinct (%)	14.0%
Missing	0
Missing (%)	0.0%
Memory size	18.0 KiB

[]	253
	5
["cheaper"]	2
["refunds"]	2
["$300","settlement"]	1
Other values (37)	37

Length

Max length	37
Median length	2
Mean length	4.1
Min length	0

Characters and Unicode

Total characters	1230
Distinct characters	53
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	38 ?
Unique (%)	12.7%

Sample

1st row	[]
2nd row	[]
3rd row	[]
4th row	[]
5th row	[]

Common Values

Value	Count	Frequency (%)
[]	253	84.3%
	5	1.7%
["cheaper"]	2	0.7%
["refunds"]	2	0.7%
["$300","settlement"]	1	0.3%
["buy","Dollar Tree"]	1	0.3%
["$1.8 Million"]	1	0.3%
["$5 Million"]	1	0.3%
["Full Benefits"]	1	0.3%
["$5 billion"]	1	0.3%
Other values (32)	32	10.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	253	78.6%
million	5	1.6%
refunds	3	0.9%
billion	3	0.9%
generous	2	0.6%
benefits	2	0.6%
5	2	0.6%
cheaper	2	0.6%
fines	2	0.6%
1	2	0.6%
Other values (46)	46	14.3%

Most occurring characters

Value	Count	Frequency (%)
[	295	24.0%
]	295	24.0%
"	116	9.4%
e	45	3.7%
i	43	3.5%
l	36	2.9%
n	32	2.6%
o	29	2.4%
s	28	2.3%
	27	2.2%
Other values (43)	284	23.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1230	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	295	24.0%
]	295	24.0%
"	116	9.4%
e	45	3.7%
i	43	3.5%
l	36	2.9%
n	32	2.6%
o	29	2.4%
s	28	2.3%
	27	2.2%
Other values (43)	284	23.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1230	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	295	24.0%
]	295	24.0%
"	116	9.4%
e	45	3.7%
i	43	3.5%
l	36	2.9%
n	32	2.6%
o	29	2.4%
s	28	2.3%
	27	2.2%
Other values (43)	284	23.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1230	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	295	24.0%
]	295	24.0%
"	116	9.4%
e	45	3.7%
i	43	3.5%
l	36	2.9%
n	32	2.6%
o	29	2.4%
s	28	2.3%
	27	2.2%
Other values (43)	284	23.1%

Prohibition_Restriction_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.4 KiB

No	265
Yes	30
	5

Length

Max length	3
Median length	2
Mean length	2.0666667
Min length	0

Characters and Unicode

Total characters	620
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	Yes
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	265	88.3%
Yes	30	10.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	265	89.8%
yes	30	10.2%

Most occurring characters

Value	Count	Frequency (%)
N	265	42.7%
o	265	42.7%
Y	30	4.8%
e	30	4.8%
s	30	4.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	620	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	265	42.7%
o	265	42.7%
Y	30	4.8%
e	30	4.8%
s	30	4.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	620	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	265	42.7%
o	265	42.7%
Y	30	4.8%
e	30	4.8%
s	30	4.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	620	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	265	42.7%
o	265	42.7%
Y	30	4.8%
e	30	4.8%
s	30	4.8%

Prohibition_Restriction_Words
Categorical

High correlation Imbalance

Distinct	30
Distinct (%)	10.0%
Missing	0
Missing (%)	0.0%
Memory size	18.0 KiB

[]	265
	5
["Banned"]	2
["bans"]	2
["ending the benefit","without immediate renewal"]	1
Other values (25)	25

Length

Max length	68
Median length	2
Mean length	3.8733333
Min length	0

Characters and Unicode

Total characters	1162
Distinct characters	41
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	26 ?
Unique (%)	8.7%

Sample

1st row	[]
2nd row	[]
3rd row	["denied"]
4th row	["ending the benefit","without immediate renewal"]
5th row	[]

Common Values

Value	Count	Frequency (%)
[]	265	88.3%
	5	1.7%
["Banned"]	2	0.7%
["bans"]	2	0.7%
["ending the benefit","without immediate renewal"]	1	0.3%
["will have to meet new requirements","keep their driver's license"]	1	0.3%
["Too Risqué","for Radio Play"]	1	0.3%
["Goodbye","illegal","no longer be used"]	1	0.3%
["Dispose of Items"]	1	0.3%
["Neither"]	1	0.3%
Other values (20)	20	6.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	265	78.9%
to	3	0.9%
banned	2	0.6%
the	2	0.6%
bans	2	0.6%
neither	2	0.6%
away	2	0.6%
renewal	1	0.3%
benefit","without	1	0.3%
will	1	0.3%
Other values (55)	55	16.4%

Most occurring characters

Value	Count	Frequency (%)
[	295	25.4%
]	295	25.4%
"	82	7.1%
e	66	5.7%
n	41	3.5%
	41	3.5%
i	37	3.2%
o	34	2.9%
t	29	2.5%
r	27	2.3%
Other values (31)	215	18.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1162	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
[	295	25.4%
]	295	25.4%
"	82	7.1%
e	66	5.7%
n	41	3.5%
	41	3.5%
i	37	3.2%
o	34	2.9%
t	29	2.5%
r	27	2.3%
Other values (31)	215	18.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1162	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
[	295	25.4%
]	295	25.4%
"	82	7.1%
e	66	5.7%
n	41	3.5%
	41	3.5%
i	37	3.2%
o	34	2.9%
t	29	2.5%
r	27	2.3%
Other values (31)	215	18.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1162	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
[	295	25.4%
]	295	25.4%
"	82	7.1%
e	66	5.7%
n	41	3.5%
	41	3.5%
i	37	3.2%
o	34	2.9%
t	29	2.5%
r	27	2.3%
Other values (31)	215	18.5%

National_Relevance_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	189
Yes	106
	5

Length

Max length	3
Median length	2
Mean length	2.32
Min length	0

Characters and Unicode

Total characters	696
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	Yes
5th row	No

Common Values

Value	Count	Frequency (%)
No	189	63.0%
Yes	106	35.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	189	64.1%
yes	106	35.9%

Most occurring characters

Value	Count	Frequency (%)
N	189	27.2%
o	189	27.2%
Y	106	15.2%
e	106	15.2%
s	106	15.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	696	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	189	27.2%
o	189	27.2%
Y	106	15.2%
e	106	15.2%
s	106	15.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	696	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	189	27.2%
o	189	27.2%
Y	106	15.2%
e	106	15.2%
s	106	15.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	696	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	189	27.2%
o	189	27.2%
Y	106	15.2%
e	106	15.2%
s	106	15.2%

National_Relevance_Words
Text

Distinct	74
Distinct (%)	24.7%
Missing	0
Missing (%)	0.0%
Memory size	19.1 KiB

Length

Max length	113
Median length	2
Mean length	7.7533333
Min length	0

Characters and Unicode

Total characters	2326
Distinct characters	56
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	61 ?
Unique (%)	20.3%

Sample

1st row	[]
2nd row	["U.S."]
3rd row	[]
4th row	["United States"]
5th row	[]

Value	Count	Frequency (%)
	189	54.8%
states	11	3.2%
new	11	3.2%
york	10	2.9%
state	10	2.9%
ny	6	1.7%
us	6	1.7%
california	5	1.4%
u.s	5	1.4%
united	4	1.2%
Other values (77)	88	25.5%

Most occurring characters

Value	Count	Frequency (%)
"	346	14.9%
[	295	12.7%
]	295	12.7%
a	141	6.1%
i	105	4.5%
e	103	4.4%
t	103	4.4%
n	80	3.4%
r	78	3.4%
,	67	2.9%
Other values (46)	713	30.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2326	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
"	346	14.9%
[	295	12.7%
]	295	12.7%
a	141	6.1%
i	105	4.5%
e	103	4.4%
t	103	4.4%
n	80	3.4%
r	78	3.4%
,	67	2.9%
Other values (46)	713	30.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2326	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
"	346	14.9%
[	295	12.7%
]	295	12.7%
a	141	6.1%
i	105	4.5%
e	103	4.4%
t	103	4.4%
n	80	3.4%
r	78	3.4%
,	67	2.9%
Other values (46)	713	30.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2326	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
"	346	14.9%
[	295	12.7%
]	295	12.7%
a	141	6.1%
i	105	4.5%
e	103	4.4%
t	103	4.4%
n	80	3.4%
r	78	3.4%
,	67	2.9%
Other values (46)	713	30.7%

Recognized_Brand_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	177
Yes	118
	5

Length

Max length	3
Median length	2
Mean length	2.36
Min length	0

Characters and Unicode

Total characters	708
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
No	177	59.0%
Yes	118	39.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	177	60.0%
yes	118	40.0%

Most occurring characters

Value	Count	Frequency (%)
N	177	25.0%
o	177	25.0%
Y	118	16.7%
e	118	16.7%
s	118	16.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	708	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	177	25.0%
o	177	25.0%
Y	118	16.7%
e	118	16.7%
s	118	16.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	708	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	177	25.0%
o	177	25.0%
Y	118	16.7%
e	118	16.7%
s	118	16.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	708	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	177	25.0%
o	177	25.0%
Y	118	16.7%
e	118	16.7%
s	118	16.7%

Recognized_Brand_Words
Text

Distinct	95
Distinct (%)	31.7%
Missing	0
Missing (%)	0.0%
Memory size	19.6 KiB

Length

Max length	118
Median length	2
Mean length	9.1533333
Min length	0

Characters and Unicode

Total characters	2746
Distinct characters	69
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	83 ?
Unique (%)	27.7%

Sample

1st row	["Ford"]
2nd row	[]
3rd row	[]
4th row	["DMV"]
5th row	["McDonald's"]

Value	Count	Frequency (%)
	177	44.8%
security	8	2.0%
social	7	1.8%
walmart	7	1.8%
tsa	5	1.3%
mcdonald's	3	0.8%
costco	3	0.8%
air	3	0.8%
tree	2	0.5%
walgreens	2	0.5%
Other values (162)	178	45.1%

Most occurring characters

Value	Count	Frequency (%)
"	364	13.3%
[	295	10.7%
]	295	10.7%
e	146	5.3%
a	141	5.1%
r	114	4.2%
i	102	3.7%
	100	3.6%
o	98	3.6%
l	89	3.2%
Other values (59)	1002	36.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2746	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
"	364	13.3%
[	295	10.7%
]	295	10.7%
e	146	5.3%
a	141	5.1%
r	114	4.2%
i	102	3.7%
	100	3.6%
o	98	3.6%
l	89	3.2%
Other values (59)	1002	36.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2746	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
"	364	13.3%
[	295	10.7%
]	295	10.7%
e	146	5.3%
a	141	5.1%
r	114	4.2%
i	102	3.7%
	100	3.6%
o	98	3.6%
l	89	3.2%
Other values (59)	1002	36.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2746	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
"	364	13.3%
[	295	10.7%
]	295	10.7%
e	146	5.3%
a	141	5.1%
r	114	4.2%
i	102	3.7%
	100	3.6%
o	98	3.6%
l	89	3.2%
Other values (59)	1002	36.5%

Curiosity_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

Yes	222
No	73
	5

Length

Max length	3
Median length	3
Mean length	2.7066667
Min length	0

Characters and Unicode

Total characters	812
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	No
4th row	No
5th row	Yes

Common Values

Value	Count	Frequency (%)
Yes	222	74.0%
No	73	24.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
yes	222	75.3%
no	73	24.7%

Most occurring characters

Value	Count	Frequency (%)
Y	222	27.3%
e	222	27.3%
s	222	27.3%
N	73	9.0%
o	73	9.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	812	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
Y	222	27.3%
e	222	27.3%
s	222	27.3%
N	73	9.0%
o	73	9.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	812	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
Y	222	27.3%
e	222	27.3%
s	222	27.3%
N	73	9.0%
o	73	9.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	812	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
Y	222	27.3%
e	222	27.3%
s	222	27.3%
N	73	9.0%
o	73	9.0%

Curiosity_Evidence
Text

Distinct	223
Distinct (%)	74.3%
Missing	0
Missing (%)	0.0%
Memory size	51.1 KiB

Length

Max length	273
Median length	190
Mean length	104.4
Min length	0

Characters and Unicode

Total characters	31320
Distinct characters	82
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	222 ?
Unique (%)	74.0%

Sample

1st row
2nd row	The phrase 'Essential Changes' prompts readers to seek information on what those changes entail.
3rd row
4th row
5th row	The headline creates an information gap by not revealing which specific breakfast item is being removed.

Value	Count	Frequency (%)
the	495	9.9%
and	167	3.3%
to	165	3.3%
an	154	3.1%
gap	153	3.1%
information	151	3.0%
creates	143	2.9%
reader	111	2.2%
about	109	2.2%
a	84	1.7%
Other values (1078)	3272	65.4%

Most occurring characters

Value	Count	Frequency (%)
	4782	15.3%
e	3100	9.9%
a	2342	7.5%
t	2149	6.9%
i	1858	5.9%
n	1855	5.9%
r	1781	5.7%
o	1770	5.7%
s	1498	4.8%
h	1266	4.0%
Other values (72)	8919	28.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	31320	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	4782	15.3%
e	3100	9.9%
a	2342	7.5%
t	2149	6.9%
i	1858	5.9%
n	1855	5.9%
r	1781	5.7%
o	1770	5.7%
s	1498	4.8%
h	1266	4.0%
Other values (72)	8919	28.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	31320	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	4782	15.3%
e	3100	9.9%
a	2342	7.5%
t	2149	6.9%
i	1858	5.9%
n	1855	5.9%
r	1781	5.7%
o	1770	5.7%
s	1498	4.8%
h	1266	4.0%
Other values (72)	8919	28.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	31320	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	4782	15.3%
e	3100	9.9%
a	2342	7.5%
t	2149	6.9%
i	1858	5.9%
n	1855	5.9%
r	1781	5.7%
o	1770	5.7%
s	1498	4.8%
h	1266	4.0%
Other values (72)	8919	28.5%

Fear_Concern_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	164
Yes	131
	5

Length

Max length	3
Median length	2
Mean length	2.4033333
Min length	0

Characters and Unicode

Total characters	721
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	No
3rd row	No
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
No	164	54.7%
Yes	131	43.7%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	164	55.6%
yes	131	44.4%

Most occurring characters

Value	Count	Frequency (%)
N	164	22.7%
o	164	22.7%
Y	131	18.2%
e	131	18.2%
s	131	18.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	721	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	164	22.7%
o	164	22.7%
Y	131	18.2%
e	131	18.2%
s	131	18.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	721	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	164	22.7%
o	164	22.7%
Y	131	18.2%
e	131	18.2%
s	131	18.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	721	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	164	22.7%
o	164	22.7%
Y	131	18.2%
e	131	18.2%
s	131	18.2%

Fear_Concern_Evidence
Text

Distinct	133
Distinct (%)	44.3%
Missing	0
Missing (%)	0.0%
Memory size	34.5 KiB

Length

Max length	240
Median length	0
Mean length	57.16
Min length	0

Characters and Unicode

Total characters	17148
Distinct characters	69
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	132 ?
Unique (%)	44.0%

Sample

1st row	The phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.
2nd row
3rd row
4th row	The phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.
5th row	The phrase 'Removing... for Good' could evoke concern among customers about a favorite item.

Value	Count	Frequency (%)
the	172	6.7%
and	116	4.5%
concern	116	4.5%
of	81	3.2%
a	74	2.9%
or	62	2.4%
for	60	2.3%
about	52	2.0%
evoke	50	2.0%
to	47	1.8%
Other values (771)	1730	67.6%

Most occurring characters

Value	Count	Frequency (%)
	2428	14.2%
e	1746	10.2%
n	1232	7.2%
o	1125	6.6%
a	1087	6.3%
t	998	5.8%
i	987	5.8%
r	965	5.6%
s	858	5.0%
c	742	4.3%
Other values (59)	4980	29.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	17148	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	2428	14.2%
e	1746	10.2%
n	1232	7.2%
o	1125	6.6%
a	1087	6.3%
t	998	5.8%
i	987	5.8%
r	965	5.6%
s	858	5.0%
c	742	4.3%
Other values (59)	4980	29.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	17148	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	2428	14.2%
e	1746	10.2%
n	1232	7.2%
o	1125	6.6%
a	1087	6.3%
t	998	5.8%
i	987	5.8%
r	965	5.6%
s	858	5.0%
c	742	4.3%
Other values (59)	4980	29.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	17148	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	2428	14.2%
e	1746	10.2%
n	1232	7.2%
o	1125	6.6%
a	1087	6.3%
t	998	5.8%
i	987	5.8%
r	965	5.6%
s	858	5.0%
c	742	4.3%
Other values (59)	4980	29.0%

Surprise_Awe_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	197
Yes	98
	5

Length

Max length	3
Median length	2
Mean length	2.2933333
Min length	0

Characters and Unicode

Total characters	688
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	197	65.7%
Yes	98	32.7%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	197	66.8%
yes	98	33.2%

Most occurring characters

Value	Count	Frequency (%)
N	197	28.6%
o	197	28.6%
Y	98	14.2%
e	98	14.2%
s	98	14.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	688	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	197	28.6%
o	197	28.6%
Y	98	14.2%
e	98	14.2%
s	98	14.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	688	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	197	28.6%
o	197	28.6%
Y	98	14.2%
e	98	14.2%
s	98	14.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	688	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	197	28.6%
o	197	28.6%
Y	98	14.2%
e	98	14.2%
s	98	14.2%

Surprise_Awe_Evidence
Text

Distinct	99
Distinct (%)	33.0%
Missing	0
Missing (%)	0.0%
Memory size	29.6 KiB

Length

Max length	206
Median length	0
Mean length	38.413333
Min length	0

Characters and Unicode

Total characters	11524
Distinct characters	76
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	98 ?
Unique (%)	32.7%

Sample

1st row
2nd row
3rd row
4th row
5th row

Value	Count	Frequency (%)
the	165	9.0%
of	99	5.4%
and	84	4.6%
a	79	4.3%
surprise	50	2.7%
is	32	1.7%
evoke	32	1.7%
can	32	1.7%
unexpected	31	1.7%
surprising	30	1.6%
Other values (660)	1197	65.4%

Most occurring characters

Value	Count	Frequency (%)
	1733	15.0%
e	1211	10.5%
n	752	6.5%
a	728	6.3%
i	710	6.2%
s	683	5.9%
t	645	5.6%
o	602	5.2%
r	588	5.1%
l	347	3.0%
Other values (66)	3525	30.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11524	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1733	15.0%
e	1211	10.5%
n	752	6.5%
a	728	6.3%
i	710	6.2%
s	683	5.9%
t	645	5.6%
o	602	5.2%
r	588	5.1%
l	347	3.0%
Other values (66)	3525	30.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11524	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1733	15.0%
e	1211	10.5%
n	752	6.5%
a	728	6.3%
i	710	6.2%
s	683	5.9%
t	645	5.6%
o	602	5.2%
r	588	5.1%
l	347	3.0%
Other values (66)	3525	30.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11524	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1733	15.0%
e	1211	10.5%
n	752	6.5%
a	728	6.3%
i	710	6.2%
s	683	5.9%
t	645	5.6%
o	602	5.2%
r	588	5.1%
l	347	3.0%
Other values (66)	3525	30.6%

Indignation_Controversy_Present
Categorical

High correlation Imbalance

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	242
Yes	53
	5

Length

Max length	3
Median length	2
Mean length	2.1433333
Min length	0

Characters and Unicode

Total characters	643
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	Yes
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	242	80.7%
Yes	53	17.7%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	242	82.0%
yes	53	18.0%

Most occurring characters

Value	Count	Frequency (%)
N	242	37.6%
o	242	37.6%
Y	53	8.2%
e	53	8.2%
s	53	8.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	242	37.6%
o	242	37.6%
Y	53	8.2%
e	53	8.2%
s	53	8.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	242	37.6%
o	242	37.6%
Y	53	8.2%
e	53	8.2%
s	53	8.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	643	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	242	37.6%
o	242	37.6%
Y	53	8.2%
e	53	8.2%
s	53	8.2%

Indignation_Controversy_Evidence
Text

Distinct	54
Distinct (%)	18.0%
Missing	0
Missing (%)	0.0%
Memory size	25.7 KiB

Length

Max length	253
Median length	0
Mean length	23.96
Min length	0

Characters and Unicode

Total characters	7188
Distinct characters	68
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	53 ?
Unique (%)	17.7%

Sample

1st row
2nd row
3rd row	The phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.
4th row
5th row

Value	Count	Frequency (%)
the	65	6.1%
and	53	5.0%
of	36	3.4%
a	36	3.4%
indignation	35	3.3%
to	29	2.7%
debate	28	2.6%
provoke	27	2.5%
or	26	2.4%
can	20	1.9%
Other values (425)	713	66.8%

Most occurring characters

Value	Count	Frequency (%)
	1015	14.1%
e	646	9.0%
n	567	7.9%
i	524	7.3%
a	515	7.2%
o	507	7.1%
t	455	6.3%
r	364	5.1%
s	329	4.6%
d	257	3.6%
Other values (58)	2009	27.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	7188	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1015	14.1%
e	646	9.0%
n	567	7.9%
i	524	7.3%
a	515	7.2%
o	507	7.1%
t	455	6.3%
r	364	5.1%
s	329	4.6%
d	257	3.6%
Other values (58)	2009	27.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	7188	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1015	14.1%
e	646	9.0%
n	567	7.9%
i	524	7.3%
a	515	7.2%
o	507	7.1%
t	455	6.3%
r	364	5.1%
s	329	4.6%
d	257	3.6%
Other values (58)	2009	27.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	7188	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1015	14.1%
e	646	9.0%
n	567	7.9%
i	524	7.3%
a	515	7.2%
o	507	7.1%
t	455	6.3%
r	364	5.1%
s	329	4.6%
d	257	3.6%
Other values (58)	2009	27.9%

Hope_Optimism_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.5 KiB

No	211
Yes	84
	5

Length

Max length	3
Median length	2
Mean length	2.2466667
Min length	0

Characters and Unicode

Total characters	674
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	211	70.3%
Yes	84	28.0%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
no	211	71.5%
yes	84	28.5%

Most occurring characters

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	674	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	674	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	674	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	211	31.3%
o	211	31.3%
Y	84	12.5%
e	84	12.5%
s	84	12.5%

Hope_Optimism_Evidence
Text

Distinct	85
Distinct (%)	28.3%
Missing	0
Missing (%)	0.0%
Memory size	26.0 KiB

Length

Max length	186
Median length	0
Mean length	31.47
Min length	0

Characters and Unicode

Total characters	9441
Distinct characters	62
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	84 ?
Unique (%)	28.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Value	Count	Frequency (%)
a	117	8.1%
the	86	5.9%
and	62	4.3%
for	61	4.2%
of	54	3.7%
positive	44	3.0%
hope	42	2.9%
to	30	2.1%
offers	23	1.6%
sense	18	1.2%
Other values (502)	912	62.9%

Most occurring characters

Value	Count	Frequency (%)
	1365	14.5%
e	1034	11.0%
o	714	7.6%
i	615	6.5%
a	538	5.7%
s	535	5.7%
t	514	5.4%
n	501	5.3%
r	487	5.2%
l	286	3.0%
Other values (52)	2852	30.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	9441	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1365	14.5%
e	1034	11.0%
o	714	7.6%
i	615	6.5%
a	538	5.7%
s	535	5.7%
t	514	5.4%
n	501	5.3%
r	487	5.2%
l	286	3.0%
Other values (52)	2852	30.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	9441	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1365	14.5%
e	1034	11.0%
o	714	7.6%
i	615	6.5%
a	538	5.7%
s	535	5.7%
t	514	5.4%
n	501	5.3%
r	487	5.2%
l	286	3.0%
Other values (52)	2852	30.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	9441	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1365	14.5%
e	1034	11.0%
o	714	7.6%
i	615	6.5%
a	538	5.7%
s	535	5.7%
t	514	5.4%
n	501	5.3%
r	487	5.2%
l	286	3.0%
Other values (52)	2852	30.2%

Personal_Identification_Present
Categorical

High correlation

Distinct	3
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	17.6 KiB

Yes	231
No	64
	5

Length

Max length	3
Median length	3
Mean length	2.7366667
Min length	0

Characters and Unicode

Total characters	821
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	Yes
3rd row	Yes
4th row	Yes
5th row	Yes

Common Values

Value	Count	Frequency (%)
Yes	231	77.0%
No	64	21.3%
	5	1.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
yes	231	78.3%
no	64	21.7%

Most occurring characters

Value	Count	Frequency (%)
Y	231	28.1%
e	231	28.1%
s	231	28.1%
N	64	7.8%
o	64	7.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	821	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
Y	231	28.1%
e	231	28.1%
s	231	28.1%
N	64	7.8%
o	64	7.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	821	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
Y	231	28.1%
e	231	28.1%
s	231	28.1%
N	64	7.8%
o	64	7.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	821	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
Y	231	28.1%
e	231	28.1%
s	231	28.1%
N	64	7.8%
o	64	7.8%

Personal_Identification_Evidence
Text

Distinct	232
Distinct (%)	77.3%
Missing	0
Missing (%)	0.0%
Memory size	47.3 KiB

Length

Max length	242
Median length	172
Mean length	101.12333
Min length	0

Characters and Unicode

Total characters	30337
Distinct characters	73
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	231 ?
Unique (%)	77.0%

Sample

1st row
2nd row	Directly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
3rd row	Readers who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
4th row	The terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
5th row	Appeals directly to regular McDonald's breakfast consumers who might be affected by the change.

Value	Count	Frequency (%)
the	328	7.1%
to	176	3.8%
of	169	3.7%
and	169	3.7%
a	116	2.5%
or	115	2.5%
directly	111	2.4%
with	105	2.3%
in	78	1.7%
personal	67	1.5%
Other values (991)	3185	69.0%

Most occurring characters

Value	Count	Frequency (%)
	4388	14.5%
e	3293	10.9%
t	2062	6.8%
i	2035	6.7%
a	1954	6.4%
n	1889	6.2%
o	1799	5.9%
r	1718	5.7%
s	1590	5.2%
l	1197	3.9%
Other values (63)	8412	27.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	30337	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	4388	14.5%
e	3293	10.9%
t	2062	6.8%
i	2035	6.7%
a	1954	6.4%
n	1889	6.2%
o	1799	5.9%
r	1718	5.7%
s	1590	5.2%
l	1197	3.9%
Other values (63)	8412	27.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	30337	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	4388	14.5%
e	3293	10.9%
t	2062	6.8%
i	2035	6.7%
a	1954	6.4%
n	1889	6.2%
o	1799	5.9%
r	1718	5.7%
s	1590	5.2%
l	1197	3.9%
Other values (63)	8412	27.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	30337	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	4388	14.5%
e	3293	10.9%
t	2062	6.8%
i	2035	6.7%
a	1954	6.4%
n	1889	6.2%
o	1799	5.9%
r	1718	5.7%
s	1590	5.2%
l	1197	3.9%
Other values (63)	8412	27.7%

Interactions

Correlations

	Authority_Present	Clarity_and_Conciseness_Value	Contains_Colon	Contains_Exclamation_Mark	Contains_Hyphen	Contains_Numbers	Contains_Question_Mark	Contains_Quotes	Curiosity_Present	Economic_Benefit_Present	Economic_Benefit_Words	Emphatic_Capitalization_Usage	Ends_With_Question_Mark	Exclusivity_Present	Exclusivity_Words	Fear_Concern_Present	Hope_Optimism_Present	Indignation_Controversy_Present	Length_General_Assessment	Main_Category	Main_Classification	National_Relevance_Present	Originality_and_Differentiation_Value	Personal_Identification_Present	Prohibition_Restriction_Present	Prohibition_Restriction_Words	Recognized_Brand_Present	Relevance_and_Timeliness_Value	Solution_Present	Solution_Words	Starts_With_Number	Strategic_Keyword_Usage_Value	Subcategory_2	Surprise_Awe_Present	Temporal_Urgency_Present	Temporal_Urgency_Words	Visibility
Authority_Present	1.000	0.705	0.705	0.998	0.710	0.705	0.711	0.705	0.705	0.709	0.675	0.686	0.710	0.705	0.701	0.711	0.705	0.708	0.706	0.711	0.718	0.707	0.711	0.707	0.717	0.697	0.705	0.706	0.706	0.659	0.706	0.708	0.685	0.706	0.705	0.674	1.000
Clarity_and_Conciseness_Value	0.705	1.000	0.704	0.997	0.703	0.704	0.703	0.704	0.704	0.711	0.804	0.532	0.703	0.702	0.560	0.704	0.703	0.703	0.774	0.586	0.524	0.705	0.589	0.707	0.703	0.489	0.704	0.702	0.703	0.415	0.703	0.720	0.493	0.703	0.710	0.746	1.000
Contains_Colon	0.705	0.704	1.000	0.998	0.712	0.706	0.705	0.709	0.719	0.708	0.682	0.697	0.707	0.711	0.717	0.707	0.705	0.705	0.709	0.698	0.768	0.705	0.716	0.711	0.705	0.675	0.710	0.706	0.709	0.680	0.705	0.705	0.704	0.719	0.707	0.696	1.000
Contains_Exclamation_Mark	0.998	0.997	0.998	1.000	0.998	0.998	0.998	0.998	0.998	0.998	0.930	0.975	0.998	0.998	0.990	0.998	0.998	0.998	0.998	0.978	0.968	0.998	0.997	0.998	0.998	0.952	0.998	0.998	0.998	0.916	0.998	0.998	0.945	0.998	0.998	0.941	1.000
Contains_Hyphen	0.710	0.703	0.712	0.998	1.000	0.706	0.709	0.710	0.706	0.707	0.668	0.692	0.708	0.707	0.707	0.705	0.712	0.712	0.706	0.711	0.700	0.705	0.710	0.706	0.707	0.689	0.705	0.705	0.705	0.646	0.705	0.705	0.688	0.705	0.705	0.677	1.000
Contains_Numbers	0.705	0.704	0.706	0.998	0.706	1.000	0.711	0.705	0.709	0.717	0.661	0.689	0.709	0.705	0.701	0.705	0.705	0.705	0.705	0.702	0.702	0.705	0.709	0.706	0.707	0.673	0.705	0.705	0.710	0.652	0.733	0.705	0.672	0.717	0.705	0.673	1.000
Contains_Question_Mark	0.711	0.703	0.705	0.998	0.709	0.711	1.000	0.707	0.710	0.705	0.639	0.671	0.956	0.705	0.693	0.705	0.708	0.708	0.706	0.703	0.969	0.706	0.706	0.709	0.706	0.639	0.715	0.705	0.705	0.680	0.706	0.705	0.646	0.707	0.707	0.624	1.000
Contains_Quotes	0.705	0.704	0.709	0.998	0.710	0.705	0.707	1.000	0.711	0.705	0.644	0.720	0.707	0.705	0.702	0.711	0.705	0.706	0.705	0.708	0.772	0.709	0.726	0.706	0.706	0.691	0.707	0.708	0.707	0.628	0.707	0.707	0.680	0.714	0.705	0.661	1.000
Curiosity_Present	0.705	0.704	0.719	0.998	0.706	0.709	0.710	0.711	1.000	0.705	0.656	0.687	0.709	0.705	0.699	0.719	0.705	0.706	0.705	0.732	0.713	0.725	0.744	0.706	0.706	0.668	0.705	0.705	0.705	0.650	0.705	0.705	0.683	0.730	0.706	0.669	1.000
Economic_Benefit_Present	0.709	0.711	0.708	0.998	0.707	0.717	0.705	0.705	0.705	1.000	0.932	0.681	0.705	0.705	0.701	0.711	0.725	0.709	0.712	0.714	0.682	0.706	0.708	0.708	0.707	0.659	0.706	0.705	0.705	0.643	0.707	0.705	0.678	0.714	0.705	0.670	1.000
Economic_Benefit_Words	0.675	0.804	0.682	0.930	0.668	0.661	0.639	0.644	0.656	0.932	1.000	0.000	0.645	0.661	0.391	0.655	0.690	0.682	0.727	0.148	0.134	0.663	0.533	0.674	0.635	0.000	0.659	0.690	0.654	0.000	0.627	0.670	0.190	0.672	0.658	0.169	1.000
Emphatic_Capitalization_Usage	0.686	0.532	0.697	0.975	0.692	0.689	0.671	0.720	0.687	0.681	0.000	1.000	0.671	0.671	0.303	0.688	0.691	0.691	0.672	0.231	0.195	0.690	0.547	0.680	0.694	0.310	0.687	0.671	0.697	0.397	0.704	0.671	0.075	0.687	0.701	0.283	1.000
Ends_With_Question_Mark	0.710	0.703	0.707	0.998	0.708	0.709	0.956	0.707	0.709	0.705	0.645	0.671	1.000	0.705	0.693	0.705	0.707	0.707	0.706	0.706	0.923	0.706	0.708	0.708	0.706	0.639	0.713	0.705	0.705	0.625	0.706	0.705	0.632	0.708	0.707	0.623	1.000
Exclusivity_Present	0.705	0.702	0.711	0.998	0.707	0.705	0.705	0.705	0.705	0.705	0.661	0.671	0.705	1.000	0.992	0.706	0.705	0.705	0.712	0.695	0.667	0.705	0.704	0.705	0.705	0.691	0.706	0.705	0.705	0.641	0.705	0.705	0.661	0.707	0.705	0.676	1.000
Exclusivity_Words	0.701	0.560	0.717	0.990	0.707	0.701	0.693	0.702	0.699	0.701	0.391	0.303	0.693	0.992	1.000	0.701	0.698	0.705	0.730	0.393	0.318	0.702	0.568	0.703	0.704	0.440	0.702	0.693	0.700	0.354	0.693	0.693	0.369	0.702	0.696	0.316	1.000
Fear_Concern_Present	0.711	0.704	0.707	0.998	0.705	0.705	0.705	0.711	0.719	0.711	0.655	0.688	0.705	0.706	0.701	1.000	0.732	0.735	0.709	0.784	0.701	0.721	0.704	0.715	0.725	0.684	0.705	0.707	0.708	0.647	0.706	0.705	0.689	0.708	0.706	0.673	1.000
Hope_Optimism_Present	0.705	0.703	0.705	0.998	0.712	0.705	0.708	0.705	0.705	0.725	0.690	0.691	0.707	0.705	0.698	0.732	1.000	0.716	0.705	0.752	0.695	0.709	0.703	0.709	0.705	0.672	0.706	0.709	0.785	0.731	0.705	0.708	0.698	0.705	0.707	0.666	1.000
Indignation_Controversy_Present	0.708	0.703	0.705	0.998	0.712	0.705	0.708	0.706	0.706	0.709	0.682	0.691	0.707	0.705	0.705	0.735	0.716	1.000	0.712	0.727	0.699	0.712	0.717	0.705	0.718	0.704	0.705	0.705	0.712	0.618	0.705	0.706	0.677	0.734	0.705	0.656	1.000
Length_General_Assessment	0.706	0.774	0.709	0.998	0.706	0.705	0.706	0.705	0.705	0.712	0.727	0.672	0.706	0.712	0.730	0.709	0.705	0.712	1.000	0.693	0.693	0.707	0.703	0.705	0.705	0.660	0.707	0.705	0.705	0.630	0.706	0.706	0.705	0.705	0.705	0.670	1.000
Main_Category	0.711	0.586	0.698	0.978	0.711	0.702	0.703	0.708	0.732	0.714	0.148	0.231	0.706	0.695	0.393	0.784	0.752	0.727	0.693	1.000	0.318	0.776	0.590	0.736	0.731	0.212	0.755	0.700	0.770	0.338	0.703	0.690	0.462	0.709	0.692	0.210	1.000
Main_Classification	0.718	0.524	0.768	0.968	0.700	0.702	0.969	0.772	0.713	0.682	0.134	0.195	0.923	0.667	0.318	0.701	0.695	0.699	0.693	0.318	1.000	0.690	0.596	0.694	0.699	0.316	0.708	0.665	0.704	0.445	0.801	0.669	0.233	0.723	0.705	0.197	1.000
National_Relevance_Present	0.707	0.705	0.705	0.998	0.705	0.705	0.706	0.709	0.725	0.706	0.663	0.690	0.706	0.705	0.702	0.721	0.709	0.712	0.707	0.776	0.690	1.000	0.706	0.705	0.710	0.682	0.708	0.709	0.710	0.640	0.705	0.706	0.712	0.709	0.708	0.675	1.000
Originality_and_Differentiation_Value	0.711	0.589	0.716	0.997	0.710	0.709	0.706	0.726	0.744	0.708	0.533	0.547	0.708	0.704	0.568	0.704	0.703	0.717	0.703	0.590	0.596	0.706	1.000	0.726	0.706	0.535	0.709	0.703	0.703	0.516	0.704	0.718	0.548	0.816	0.702	0.548	1.000
Personal_Identification_Present	0.707	0.707	0.711	0.998	0.706	0.706	0.709	0.706	0.706	0.708	0.674	0.680	0.708	0.705	0.703	0.715	0.709	0.705	0.705	0.736	0.694	0.705	0.726	1.000	0.713	0.653	0.705	0.708	0.708	0.631	0.706	0.705	0.692	0.742	0.705	0.664	1.000
Prohibition_Restriction_Present	0.717	0.703	0.705	0.998	0.707	0.707	0.706	0.706	0.706	0.707	0.635	0.694	0.706	0.705	0.704	0.725	0.705	0.718	0.705	0.731	0.699	0.710	0.706	0.713	1.000	0.953	0.705	0.706	0.705	0.650	0.708	0.706	0.681	0.707	0.709	0.686	1.000
Prohibition_Restriction_Words	0.697	0.489	0.675	0.952	0.689	0.673	0.639	0.691	0.668	0.659	0.000	0.310	0.639	0.691	0.440	0.684	0.672	0.704	0.660	0.212	0.316	0.682	0.535	0.653	0.953	1.000	0.674	0.638	0.675	0.220	0.640	0.638	0.000	0.679	0.694	0.275	1.000
Recognized_Brand_Present	0.705	0.704	0.710	0.998	0.705	0.705	0.715	0.707	0.705	0.706	0.659	0.687	0.713	0.706	0.702	0.705	0.706	0.705	0.707	0.755	0.708	0.708	0.709	0.705	0.705	0.674	1.000	0.705	0.718	0.643	0.715	0.705	0.689	0.705	0.706	0.668	1.000
Relevance_and_Timeliness_Value	0.706	0.702	0.706	0.998	0.705	0.705	0.705	0.708	0.705	0.705	0.690	0.671	0.705	0.705	0.693	0.707	0.709	0.705	0.705	0.700	0.665	0.709	0.703	0.708	0.706	0.638	0.705	1.000	0.707	0.585	0.706	0.727	0.711	0.705	0.707	0.623	1.000
Solution_Present	0.706	0.703	0.709	0.998	0.705	0.710	0.705	0.707	0.705	0.705	0.654	0.697	0.705	0.705	0.700	0.708	0.785	0.712	0.705	0.770	0.704	0.710	0.703	0.708	0.705	0.675	0.718	0.707	1.000	0.917	0.705	0.706	0.717	0.708	0.705	0.665	1.000
Solution_Words	0.659	0.415	0.680	0.916	0.646	0.652	0.680	0.628	0.650	0.643	0.000	0.397	0.625	0.641	0.354	0.647	0.731	0.618	0.630	0.338	0.445	0.640	0.516	0.631	0.650	0.220	0.643	0.585	0.917	1.000	0.658	0.584	0.291	0.640	0.635	0.097	1.000
Starts_With_Number	0.706	0.703	0.705	0.998	0.705	0.733	0.706	0.707	0.705	0.707	0.627	0.704	0.706	0.705	0.693	0.706	0.705	0.705	0.706	0.703	0.801	0.705	0.704	0.706	0.708	0.640	0.715	0.706	0.705	0.658	1.000	0.705	0.693	0.705	0.710	0.626	1.000
Strategic_Keyword_Usage_Value	0.708	0.720	0.705	0.998	0.705	0.705	0.705	0.707	0.705	0.705	0.670	0.671	0.705	0.705	0.693	0.705	0.708	0.706	0.706	0.690	0.669	0.706	0.718	0.705	0.706	0.638	0.705	0.727	0.706	0.584	0.705	1.000	0.630	0.706	0.706	0.622	1.000
Subcategory_2	0.685	0.493	0.704	0.945	0.688	0.672	0.646	0.680	0.683	0.678	0.190	0.075	0.632	0.661	0.369	0.689	0.698	0.677	0.705	0.462	0.233	0.712	0.548	0.692	0.681	0.000	0.689	0.711	0.717	0.291	0.693	0.630	1.000	0.678	0.652	0.000	1.000
Surprise_Awe_Present	0.706	0.703	0.719	0.998	0.705	0.717	0.707	0.714	0.730	0.714	0.672	0.687	0.708	0.707	0.702	0.708	0.705	0.734	0.705	0.709	0.723	0.709	0.816	0.742	0.707	0.679	0.705	0.705	0.708	0.640	0.705	0.706	0.678	1.000	0.712	0.686	1.000
Temporal_Urgency_Present	0.705	0.710	0.707	0.998	0.705	0.705	0.707	0.705	0.706	0.705	0.658	0.701	0.707	0.705	0.696	0.706	0.707	0.705	0.705	0.692	0.705	0.708	0.702	0.705	0.709	0.694	0.706	0.707	0.705	0.635	0.710	0.706	0.652	0.712	1.000	0.943	1.000
Temporal_Urgency_Words	0.674	0.746	0.696	0.941	0.677	0.673	0.624	0.661	0.669	0.670	0.169	0.283	0.623	0.676	0.316	0.673	0.666	0.656	0.670	0.210	0.197	0.675	0.548	0.664	0.686	0.275	0.668	0.623	0.665	0.097	0.626	0.622	0.000	0.686	0.943	1.000	1.000
Visibility	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

	Title	Visibility	Cleaned_Headline	Main_Category	Subcategory_1	Subcategory_2	Clarity_and_Conciseness_Value	Clarity_and_Conciseness_Comment	Relevance_and_Timeliness_Value	Relevance_and_Timeliness_Comment	Strategic_Keyword_Usage_Value	Strategic_Keyword_Usage_Comment	Originality_and_Differentiation_Value	Originality_and_Differentiation_Comment	Contains_Numbers	Contains_Quotes	Contains_Question_Mark	Contains_Colon	Contains_Hyphen	Contains_Exclamation_Mark	Starts_With_Number	Ends_With_Question_Mark	Length_General_Assessment	Length_Number_of_Characters	Emphatic_Capitalization_Usage	Main_Classification	Comment_Type_Justification	Temporal_Urgency_Present	Temporal_Urgency_Words	Exclusivity_Present	Exclusivity_Words	Authority_Present	Authority_Words	Solution_Present	Solution_Words	Economic_Benefit_Present	Economic_Benefit_Words	Prohibition_Restriction_Present	Prohibition_Restriction_Words	National_Relevance_Present	National_Relevance_Words	Recognized_Brand_Present	Recognized_Brand_Words	Curiosity_Present	Curiosity_Evidence	Fear_Concern_Present	Fear_Concern_Evidence	Surprise_Awe_Present	Surprise_Awe_Evidence	Indignation_Controversy_Present	Indignation_Controversy_Evidence	Hope_Optimism_Present	Hope_Optimism_Evidence	Personal_Identification_Present	Personal_Identification_Evidence
0	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand	22572066	Ford is forced to immediately shut down factories and halt car production as CEO admits 'day to day' struggle for brand	Finance_and_Business	Companies & Entrepreneurship	N/A	High	The main message is very clear and easy to understand, detailing Ford's production halt and the CEO's admission of struggle.	High	The headline discusses a significant event for a major global brand, which is highly relevant and timely for business and general news audiences.	High	Key terms like 'Ford', 'factories', 'car production', 'CEO', and 'struggle' are used effectively, making the headline highly discoverable and appealing.	Medium	While the subject matter is impactful, the phrasing is a standard news report style, not particularly unique in its linguistic approach.	No	Yes	No	No	No	No	No	No	Adequate	105	No	Declarative Simple	The headline presents a series of factual statements about Ford's situation and the CEO's admission without posing a question, using urgency markers, or comparing elements.	Yes	["immediately"]	No	[]	Yes	["CEO admits"]	No	[]	No	[]	No	[]	No	[]	Yes	["Ford"]	No		Yes	The phrases 'forced to immediately shut down factories', 'halt car production', and 'struggle for brand' evoke concern about economic stability and the future of a major company.	No		No		No		No
1	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above	21331409	New U.S. Driving License Rule for Seniors Begins July 2025 - Essential Changes for Drivers Aged 70 and Above	News_and_Current_Events	Politics	Government	High	The main message is exceptionally clear, detailing who, what, and when without ambiguity.	High	Highly relevant to a specific, large demographic (seniors/drivers) and provides timely information about a future change.	High	Contains highly relevant keywords like 'U.S. Driving License Rule', 'Seniors', 'July 2025', 'Essential Changes', and 'Drivers Aged 70 and Above'.	Medium	While a standard news announcement format, the specific details make it distinct, yet it lacks a unique angle or creative phrasing.	Yes	No	No	No	Yes	No	No	No	Adequate	101	No	Declarative Simple	The headline directly states a new rule and its implications, serving as a straightforward announcement.	Yes	["Begins July 2025"]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["U.S."]	No	[]	Yes	The phrase 'Essential Changes' prompts readers to seek information on what those changes entail.	No		No		No		No		Yes	Directly targets 'Seniors' and 'Drivers Aged 70 and Above', creating immediate relevance for this demographic.
2	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender	19344936	Cadets who met all Air Force Academy graduation standards denied commissions because they’re transgender	News_and_Current_Events	Politics	Government	High	The main message is very clear and direct, stating precisely what occurred and why.	High	The topic of transgender rights and military policy is highly current and relevant, resonating with ongoing social and political discussions.	High	Uses strong, specific keywords like 'Cadets', 'Air Force Academy', 'denied commissions', and 'transgender', which are highly relevant to the subject matter and discoverable.	Medium	The specific event described is notable, though the broader subject of transgender individuals in the military has been discussed before. It offers a distinct event within a known debate.	No	No	No	No	No	No	No	No	Adequate	106	No	Declarative Simple	The headline makes a direct statement about an event, presenting it as a fact without using a question, direct quote, or emphasizing urgency.	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["denied"]	No	[]	No	[]	No		No		No		Yes	The phrase 'denied commissions because they’re transgender' directly implies a potential act of discrimination, which is highly likely to provoke indignation and controversy among various audiences.	No		Yes	Readers who are LGBTQ+, advocates for civil rights, or those with military connections may strongly identify with the cadets' situation and the implications of the decision.
3	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently	18797641	The DMV confirms it - The United States is ending the benefit that allowed drivers to drive with an expired license without immediate renewal - millions of drivers will have to renew urgently	News_and_Current_Events	Politics	Government	High	The main message is very clear and direct, stating the change, its source, and its impact.	High	Highly relevant as it affects a large demographic ('millions of drivers') and requires urgent action, indicating timeliness.	High	Uses strong keywords like 'DMV', 'United States', 'expired license', 'drivers', and 'renew urgently', which are highly searchable and relevant.	Medium	While the specific policy change is unique, the headline structure (authority confirms X - consequence) is common. Its strength lies in the immediate, widespread impact.	Yes	No	No	No	Yes	No	No	No	Adequate	173	No	Urgency	The headline clearly states a new policy confirmed by an authority and strongly emphasizes the immediate, widespread need for action, particularly with the phrase 'renew urgently' affecting 'millions of drivers'.	Yes	["immediate","urgently"]	No	[]	Yes	["DMV","confirms"]	No	[]	No	[]	Yes	["ending the benefit","without immediate renewal"]	Yes	["United States"]	Yes	["DMV"]	No		Yes	The phrase 'millions of drivers will have to renew urgently' directly implies potential negative consequences or significant inconvenience if the reader fails to act, evoking concern.	No		No		No		Yes	The terms 'drivers' and 'millions of drivers' directly target and appeal to a very large and identifiable group of readers.
4	McDonald's Removing 1 Breakfast Menu Item for Good on July 2	16353543	McDonald's Removing 1 Breakfast Menu Item for Good on July 2	Gastronomy	Restaurants & Chefs	N/A	High	The message is clear and to the point, identifying the brand, action, and date.	High	Highly relevant for McDonald's customers and tied to a specific future date, July 2.	High	Uses strong keywords like 'McDonald's', 'Breakfast Menu', and 'Removing', appealing to target audience interests.	Medium	While common for fast-food news, the specificity of '1 Breakfast Menu Item' and 'for Good' provides some differentiation.	Yes	No	No	No	No	No	No	No	Adequate	57	No	Declarative Simple	The headline directly states a fact without posing a question, using a quote, or indicating urgency beyond the date.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["McDonald's"]	Yes	The headline creates an information gap by not revealing which specific breakfast item is being removed.	Yes	The phrase 'Removing... for Good' could evoke concern among customers about a favorite item.	No		No		No		Yes	Appeals directly to regular McDonald's breakfast consumers who might be affected by the change.
5	The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United States	15318643	The DMV confirms it—people over 70 will have to meet new requirements to keep their driver's license in the United States	News_and_Current_Events	Politics	Government	High	The main message about new DMV requirements for older drivers is very clear and easy to understand.	High	Driving regulations, especially those affecting a specific age group, are highly relevant to a large demographic and are a recurring topic of public interest.	High	Keywords like 'DMV', '70', 'driver's license', and 'United States' are highly relevant and likely to be searched for or noticed by the target audience.	Medium	The headline is specific about the authority ('DMV confirms it') and the demographic, which gives it some differentiation from generic news, but the structure is standard.	Yes	No	No	No	Yes	No	No	No	Adequate	106	No	Declarative Simple	The headline directly states a fact confirmed by an authority, informing the reader of a new regulation without posing a question or implying a mystery.	No	[]	No	[]	Yes	["confirms","DMV"]	No	[]	No	[]	Yes	["will have to meet new requirements","keep their driver's license"]	Yes	["United States"]	Yes	["DMV"]	Yes	The mention of 'new requirements' without specifying them creates an information gap, prompting readers to click to learn what these changes entail.	Yes	The phrase 'will have to meet new requirements to keep their driver's license' can evoke concern or anxiety for older drivers about their ability to retain their driving privileges and independence.	No		No		No		Yes	The headline directly addresses and impacts 'people over 70', creating strong personal identification for individuals in that age group and their families.
6	Goodbye to retirement at 65: Social Security sets a new retirement age from 2026	14627061	Goodbye to retirement at 65: Social Security sets a new retirement age from 2026	Finance_and_Business	Personal_Finance	Retirement	High	The main message is immediately clear: the retirement age is changing.	High	The topic of retirement age and Social Security is highly relevant to a broad audience and includes a specific future date ('from 2026') indicating timeliness.	High	Key terms like 'retirement,' 'Social Security,' and 'retirement age' are highly strategic and likely to be searched by interested individuals.	Medium	While retirement changes are common news, the opening phrase 'Goodbye to retirement at 65' adds an impactful and somewhat unique framing.	Yes	No	No	Yes	No	No	No	No	Adequate	70	No	Declarative Simple	The headline presents a direct statement of fact about a change in the retirement age.	Yes	["from 2026"]	No	[]	Yes	["Social Security"]	No	[]	No	[]	No	[]	No	[]	Yes	["Social Security"]	Yes	The phrase 'Goodbye to retirement at 65' immediately piques curiosity about what the new age will be and why the change is happening.	Yes	The headline could evoke concern among readers about their financial future and the implications of a later retirement.	Yes	The news of a fundamental shift in retirement age, a long-established concept, can be surprising.	Yes	Changes to social programs like retirement age frequently generate public debate and potential indignation.	No		Yes	The topic of retirement and Social Security directly affects the financial planning and future of many individuals, leading to strong personal identification.
7	Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.	13507301	Elon Musk gave Apple 72 hours to accept his $5 billion offer. Tim Cook said no, so Elon followed through with his threat.	Finance_and_Business	Companies & Entrepreneurship	N/A	High	The main message is very clear, detailing the involved parties, the offer, the rejection, and the subsequent action, without ambiguity.	High	Features highly relevant and frequently discussed figures (Elon Musk, Tim Cook) and companies (Apple), ensuring strong general interest.	High	Includes prominent keywords such as 'Elon Musk', 'Apple', 'Tim Cook', and '$5 billion', which are highly searchable and appealing to a broad audience.	High	The narrative of a public challenge, rejection, and explicit follow-through on a 'threat' provides a unique and dramatic angle that stands out.	Yes	No	No	No	No	No	No	No	Adequate	104	No	Declarative Simple	The headline presents a straightforward statement of facts without posing a question, quoting directly, or explicitly indicating urgency.	Yes	["72 hours"]	No	[]	No	[]	No	[]	Yes	["$5 billion"]	No	[]	No	[]	Yes	["Elon Musk","Apple","Tim Cook"]	Yes	The phrase 'followed through with his threat' creates an information gap, compelling the reader to wonder what the threat was and its consequences.	No		Yes	The audacious $5 billion offer with a short ultimatum and the dramatic follow-through on a 'threat' can evoke surprise.	Yes	The nature of the 'threat' and its execution can spark debate or strong opinions about the involved parties' actions.	No		No
8	90s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”	13339952	90s Country Icon’s Teeth Fall Out Mid-Performance During Washington Concert: “The Show Must Go On”	Entertainment_and_Culture	Celebrities_and_Influencers	N/A	High	The main message is straightforward and easy to understand, describing a highly unusual and specific event.	High	The headline describes an unusual and attention-grabbing incident involving a celebrity, which is highly relevant to current interests in entertainment news.	High	Keywords like "90s Country Icon," "Teeth Fall Out," and "Mid-Performance" are highly specific and engaging, likely to capture audience attention.	High	The event described is extremely unusual and therefore the headline is highly original and stands out from typical news.	Yes	Yes	No	Yes	No	No	Yes	No	Adequate	101	Yes, "The Show Must Go On", justified use as it is a direct quote.	Direct Quote	The headline prominently features a direct quote from the event, which is essential to its content and impact.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Washington"]	No	[]	Yes	The phrase "Teeth Fall Out Mid-Performance" creates a strong information gap and immediately makes the reader curious about the circumstances.	No		Yes	The highly unexpected and bizarre nature of "Teeth Fall Out Mid-Performance" elicits surprise and possibly a sense of awe at the unusual incident.	No		No		No
9	Americans who own refrigerators from 3 brands to get $300 from settlement	13255807	Americans who own refrigerators from 3 brands to get $300 from settlement	Finance_and_Business	Personal_Finance	N/A	High	The main message is very clear: certain Americans will receive money due to a settlement.	High	Directly impacts a large segment of the population (Americans who own specific appliances) and offers a tangible benefit (money from a settlement).	High	Includes highly relevant keywords like 'Americans', 'refrigerators', 'brands', '$300', and 'settlement', which are likely search terms for interested individuals.	Medium	While the specific settlement details are unique, the headline structure of 'who gets money from X' is a common news format. The unnamed brands reduce its immediate differentiation.	Yes	No	No	No	No	No	No	No	Adequate	70	No	Declarative Simple	The headline presents a direct and straightforward statement of fact without asking a question, making a comparison, or implying urgency.	No	[]	No	[]	No	[]	No	[]	Yes	["$300","settlement"]	No	[]	Yes	["Americans"]	No	[]	Yes	The mention of '3 brands' creates a curiosity gap, prompting readers to click to find out which brands are involved.	No		No		No		Yes	The prospect of receiving $300 from a settlement evokes a sense of hope and financial optimism for those who qualify.	Yes	Americans who own refrigerators' directly targets a broad demographic, encouraging self-identification and relevance.

	Title	Visibility	Cleaned_Headline	Main_Category	Subcategory_1	Subcategory_2	Clarity_and_Conciseness_Value	Clarity_and_Conciseness_Comment	Relevance_and_Timeliness_Value	Relevance_and_Timeliness_Comment	Strategic_Keyword_Usage_Value	Strategic_Keyword_Usage_Comment	Originality_and_Differentiation_Value	Originality_and_Differentiation_Comment	Contains_Numbers	Contains_Quotes	Contains_Question_Mark	Contains_Colon	Contains_Hyphen	Contains_Exclamation_Mark	Starts_With_Number	Ends_With_Question_Mark	Length_General_Assessment	Length_Number_of_Characters	Emphatic_Capitalization_Usage	Main_Classification	Comment_Type_Justification	Temporal_Urgency_Present	Temporal_Urgency_Words	Exclusivity_Present	Exclusivity_Words	Authority_Present	Authority_Words	Solution_Present	Solution_Words	Economic_Benefit_Present	Economic_Benefit_Words	Prohibition_Restriction_Present	Prohibition_Restriction_Words	National_Relevance_Present	National_Relevance_Words	Recognized_Brand_Present	Recognized_Brand_Words	Curiosity_Present	Curiosity_Evidence	Fear_Concern_Present	Fear_Concern_Evidence	Surprise_Awe_Present	Surprise_Awe_Evidence	Indignation_Controversy_Present	Indignation_Controversy_Evidence	Hope_Optimism_Present	Hope_Optimism_Evidence	Personal_Identification_Present	Personal_Identification_Evidence
290	How To Clean A Glass Top Stove, According To An Expert	2036769	How To Clean A Glass Top Stove, According To An Expert	Home_and_Lifestyle	Cleaning & Organization	Tips	High	The main message is very clear and easy to understand.	High	This is an evergreen topic highly relevant to homeowners with glass top stoves.	High	Uses common search terms like "How To Clean" and "Glass Top Stove".	Medium	While "How To" is common, the "According To An Expert" adds a degree of differentiation and credibility.	No	No	No	No	No	No	No	No	Adequate	55	No	Attribution ('according to', 'reveals')	The headline specifies that the cleaning method is "According To An Expert," emphasizing credibility and derived knowledge.	No	[]	No	[]	Yes	["Expert"]	Yes	["How To Clean"]	No	[]	No	[]	No	[]	No	[]	Yes	The "How To" structure combined with the "According To An Expert" creates an information gap, making the reader curious about the expert's specific method.	No		No		No		Yes	Implies a solution to a common household problem, offering hope for an effective cleaning method.	Yes	Appeals directly to anyone who owns or uses a glass top stove and seeks a reliable cleaning solution.
291	Shrimp, Corn, Potatoes, and Smoked Sausage Foil Pack	2032696	Shrimp, Corn, Potatoes, and Smoked Sausage Foil Pack	Gastronomy	Recipes	Main Courses	High	Clearly describes the components of the dish.	High	Evergreen topic, appealing to anyone interested in cooking and meal preparation.	High	Contains specific ingredients and cooking method as keywords, highly searchable.	Medium	Describes a common type of dish, but the combination is specific enough.	No	No	No	No	No	No	No	No	Adequate	50	No	Declarative Simple	The headline simply states what the dish is, without asking a question, making a strong claim, or implying mystery.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No		No		No		No		No		Yes	Appeals to those interested in home cooking and specific types of meals (foil packs, comfort food ingredients).
292	Government Reverses Cuts for Thousands of Retirees - Social Security Benefits Restored Under New Rule	2026378	Government Reverses Cuts for Thousands of Retirees - Social Security Benefits Restored Under New Rule	Finance_and_Business	Personal Finance	N/A	High	The headline clearly states the action (government reverses cuts) and the beneficiaries (retirees, social security benefits restored).	High	Social Security and retiree benefits are topics of broad and consistent interest, making it highly relevant.	High	Uses clear keywords like "Government", "Retirees", "Social Security Benefits", "Restored", which are highly searchable and relevant.	Medium	While clear and informative, the headline is fairly straightforward and doesn't employ a particularly unique or surprising angle. It's a standard news announcement.	Yes	No	No	No	Yes	No	No	No	Adequate	90	No	Declarative Simple	It directly states a fact or event without posing a question, quoting someone, or indicating urgency in an emphatic way.	No	[]	No	[]	Yes	["Government","Rule"]	Yes	["Restored"]	Yes	["Cuts","Benefits"]	No	[]	No	[]	Yes	["Social Security"]	No		Yes	The word 'Cuts' implies a previous negative situation, which could evoke concern.	No		No		Yes	Words like 'Reverses', 'Restored', and 'Benefits' evoke a sense of hope and relief for retirees.	Yes	Refers to 'Thousands of Retirees' and 'Social Security Benefits', directly impacting a large demographic.
293	10 phrases deeply unhappy people often use in daily conversation	2022447	10 phrases deeply unhappy people often use in daily conversation	Health_and_Wellness	Mental Health	N/A	High	The message is clear and easy to understand, directly stating the content.	High	Deals with an evergreen topic of human behavior and mental state, highly relevant to personal well-being.	High	Uses direct and relatable keywords like 'unhappy people' and 'daily conversation' that resonate with the target audience.	Medium	While listicles are common, focusing on specific 'phrases' offers a slightly distinct angle compared to general 'signs'.	Yes	No	No	No	No	No	Yes	No	Adequate	64	No	List/Numbered ('5 ways')	The headline begins with a number followed by the topic, indicating a listicle format.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	The phrase '10 phrases deeply unhappy people often use' creates an information gap, making the reader curious about what those specific phrases are.	Yes	The mention of 'deeply unhappy people' can evoke concern or self-reflection, especially if the reader identifies with or knows someone described.	No		No		No		Yes	The topic of 'unhappy people' and phrases used in 'daily conversation' encourages personal identification or identification with someone the reader knows.
294	Carnival Cruise Line suddenly cancels sailing	2019272	Carnival Cruise Line suddenly cancels sailing	Travel	Planning & Tips	N/A	High	The main message is immediately clear, stating the subject (Carnival Cruise Line) and the action (cancels sailing).	High	Highly relevant to individuals interested in travel, especially those planning or affected by cruises. It conveys immediate, impactful news.	High	Uses highly relevant keywords like "Carnival Cruise Line" and "cancels sailing", which are directly searched by affected customers or interested parties.	Medium	The headline is a straightforward factual report. While the specific event is unique, the phrasing is generic for a cancellation announcement.	No	No	No	No	No	No	No	No	Adequate	40	No	Declarative Simple	It directly informs the reader about an event without posing a question, making a comparison, or building mystery.	Yes	["suddenly"]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Carnival Cruise Line"]	Yes	The word 'suddenly' creates an information gap, making the reader wonder about the unexpected nature and reasons for the cancellation.	Yes	The cancellation of a sailing directly triggers fear and concern for passengers who may have booked or planned to travel with Carnival Cruise Line.	Yes	The term 'suddenly' implies an unexpected event, generating surprise regarding the abrupt nature of the cancellation.	Yes	A sudden cancellation, especially of a large-scale event like a cruise sailing, can evoke indignation or frustration from affected customers.	No		Yes	Readers who have booked or considered booking with Carnival Cruise Line, or who are generally interested in cruise travel, can personally identify with the impact of such news.
295	Over 300 Flights Cancelled and Delayed as Air France, American, Oman Air, Singapore, Qatar, Emirates, United and More Face New Disruptions at Heathrow, Schiphol and Charles de Gaulle Due to Operational Challenges	2018517	Over 300 Flights Cancelled and Delayed as Air France, American, Oman Air, Singapore, Qatar, Emirates, United and More Face New Disruptions at Heathrow, Schiphol and Charles de Gaulle Due to Operational Challenges	News_and_Current_Events	International	N/A	High	The main message regarding flight cancellations and delays is clear and direct, immediately conveying the core issue and identifying the affected entities.	High	Air travel disruptions are a highly relevant and timely topic for a broad audience, especially those with travel plans or connections to the mentioned airlines and airports.	High	The headline effectively uses keywords such as "Flights Cancelled," "Delayed," specific airline names, and airport names, which are highly relevant and searchable.	Medium	While the specific details are unique, the general topic of flight disruptions due to operational issues is common, making the overall formulation somewhat generic.	Yes	No	No	No	No	No	No	No	Too long, risk of truncation	225	No	Declarative Simple	The headline directly informs the reader about flight cancellations and delays, stating facts without employing rhetorical devices like questions or explicit calls to urgency.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["Air France","American","Oman Air","Singapore","Qatar","Emirates","United","Heathrow","Schiphol","Charles de Gaulle"]	No		Yes	The headline highlights "Cancelled and Delayed" flights, "New Disruptions," and "Operational Challenges," which evoke concern for travelers and their plans.	No		No		No		Yes	Individuals with travel plans or connections to the mentioned airlines and airports will personally relate to the disruption and its potential impact.
296	California Highway Patrol to launch statewide Maximum Enforcement Period this weekend to tackle issue of speeding	2014724	California Highway Patrol to launch statewide Maximum Enforcement Period this weekend to tackle issue of speeding	News_and_Current_Events	Crime & Judicial	N/A	High	The main message is clear: California Highway Patrol will have a "Maximum Enforcement Period" to address speeding this weekend.	High	Timely due to "this weekend" and relevant to drivers in California. Speeding is a persistent public safety concern.	High	California Highway Patrol, statewide, Maximum Enforcement Period, speeding, this weekend are all highly relevant keywords.	Medium	While the specific details are new, announcements about enforcement periods are common. The phrase "tackle issue of speeding" is fairly generic.	No	No	No	No	No	No	No	No	Adequate	96	Yes, 'Maximum Enforcement Period', justified use	Declarative Simple	The headline makes a direct statement about an action the California Highway Patrol will take.	Yes	["this weekend"]	No	[]	Yes	["California Highway Patrol"]	Yes	["tackle"]	No	[]	No	[]	Yes	["California","statewide"]	No	[]	No		Yes	The headline directly addresses the 'issue of speeding' and announces a 'Maximum Enforcement Period,' which implicitly warns drivers of increased vigilance and potential consequences, triggering concern.	No		No		No		Yes	The headline refers to a 'statewide' enforcement, directly impacting anyone driving in California this weekend who might be concerned about speeding or encountering enforcement.
297	250 million acres of public land to be sold off	2002469	250 million acres of public land to be sold off	News_and_Current_Events	Environment	N/A	High	The headline is very clear and directly states the core message without ambiguity.	High	The sale of public land is a topic of significant public interest and often involves current policy debates.	High	"public land" and "sold off" are highly relevant keywords. The large number "250 million acres" acts as a strong identifier.	Medium	While the topic of land sales is not new, the sheer scale of "250 million acres" makes this headline stand out.	Yes	No	No	No	No	No	Yes	No	Adequate	45	No	Declarative Simple	The headline makes a direct and unambiguous statement about an event, without posing a question or using comparative language.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	No		Yes	The phrase "sold off" combined with the immense scale ("250 million acres") of "public land" can evoke significant concern about loss or environmental impact.	Yes	The large number "250 million acres" is surprising and can evoke awe at the sheer scale of the proposed action.	Yes	The selling off of "public land" is often a controversial topic, and the headline is likely to provoke strong opinions and indignation.	No		Yes	The term "public land" directly connects to common ownership and collective interest, fostering personal identification in readers who feel a stake in such resources.
298	Congratulations Pouring In For Legendary NFL Coach Tony Dungy	1994857	Congratulations Pouring In For Legendary NFL Coach Tony Dungy	Sports	Football	N/A	High	The main message is straightforward and easy to understand.	High	News about a prominent NFL figure like Tony Dungy is highly relevant to sports enthusiasts.	High	Uses strong keywords like "NFL Coach" and "Tony Dungy" relevant to the target audience.	Low	The headline is quite generic for a congratulatory message and does not offer a unique angle.	No	No	No	No	No	No	No	No	Adequate	62	No	Declarative Simple	The headline is a direct statement informing the reader about congratulations being received by Tony Dungy, without any rhetorical devices or specific structural classifications.	No	[]	No	[]	No	[]	No	[]	No	[]	No	[]	Yes	["NFL"]	Yes	["NFL","Tony Dungy"]	No		No		No		No		Yes	The phrase 'Congratulations Pouring In' evokes a positive and celebratory sentiment, aligning with hope/optimism.	Yes	Fans of Tony Dungy or the NFL might feel a sense of personal connection or pride regarding the news.
299	TSA issues warning to not bring popular souvenir in your hand luggage – it'll be taken away if it's over a certain size	1993759	TSA issues warning to not bring popular souvenir in your hand luggage – it'll be taken away if it's over a certain size	Public_Safety	Alerts & Prevention	N/A	High	The message is clear and easy to understand: a specific item is warned against in hand luggage due to size restrictions.	High	Travel regulations and TSA warnings are evergreen topics that are highly relevant to a broad audience, especially travelers.	High	Keywords like "TSA", "warning", "souvenir", "hand luggage", and "taken away" are highly relevant and likely to attract the attention of those interested in travel news and regulations.	Medium	While "TSA warning" headlines are common, the specific mention of "popular souvenir" and the consequence of it being "taken away if it's over a certain size" adds a moderate level of differentiation.	No	No	No	No	Yes	No	No	No	Adequate	93	No	Declarative Simple	The headline makes a clear and direct statement informing the reader about a warning issued by the TSA regarding certain items in hand luggage.	No	[]	No	[]	Yes	["TSA","issues warning"]	No	[]	No	[]	Yes	["not bring","taken away"]	No	[]	Yes	["TSA"]	Yes	The headline creates an information gap by referring to a "popular souvenir" without specifying what it is, prompting the reader to click to discover the item.	Yes	The phrases "not bring" and "it'll be taken away" evoke concern about potential loss of property or inconvenience during travel.	No		No		No		Yes	The use of "your hand luggage" and reference to a "popular souvenir" directly addresses and implicates the reader, making the warning personally relevant.

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(unknown)

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Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values